the car :: essays research papers

Title Gran Turismo 3 A-Spec FAQ/Strategy Guide Platform PlayStation 2 Version v1.01 Authors Brett "Nemesis" Franklin / OrochiJin - Tim Garza E-Mail nemesisflipmode.com / ExScanneryahoo.com Plug http//nemmysresource.cjb.net Updated August 26th, 2001The-chart-that-shows-whats-in-this-FAQi. legitimate Stuffii. Updates/ adjustment History01. Driving Techniques car Information Ultimate Car Setup Guide Car Upgrading Tips How to earn fast cash02. usual Strategies03. Simulation Mode Getting Started License Test Guide Quick pillaging Guide Race / Prize List Tuning Information04. colonnade Mode Tracks05. FAQ (Frequently Asked Questions)06. Gameshark Codes===============================================================================i. Legal Stuff===============================================================================This FAQ can only appear on the following sites (w/out having to ask me) GameFAQS <www.gamefaqs.com> CoreMagazine <www.coremagazine.com> GameSage s <www.gamesages.com> Vgstrategies.com <http//vgstrategies.about.com> GameShark.com <www.gameshark.com> FAQ Domain <http//nemmysresource.cjb.net> Happy Puppy <http//www.happypuppy.com> ZDNet/Videogames.com <http//www.videogames.com>___________________________NOT WWW.FRESHBAKEDGAMES.COM_________________________If whateverone finds it on some(prenominal) other site or MegaGames.com, please inform me ASAP.E-Mail Address nemesisflipmode.com, ExScanneryahoo.comCopyright Copyright 2000-2001 Brett "Nemesis" Franklin and Tim Garza. This FAQand everything included within this file cannot be reproduced inany way, shape or form (physical, electronical, or otherwise) aside frombeing placed on a freely-accessible, non-commercial web page in its original, unchanged and unaltered format. This FAQ cannot be used for profitablepurposes (even if no money would be made from selling it) or promotionalpurposes. It cannot be used in any sort of commercial transaction. It cannot be given away as some sort of bonus, gift, etc., with a purchaseas this creates incentive to buy and is therefore prohibited.===============================================================================ii. Updates/Revision History===============================================================================Version 1.01 (08/26/01)------------------------- Added the Quick Prize List from Jeff Evans, which just lists each race andthe car(s) you win from it. Very easy to use, thanks JeffVersion 1.00 (08/19/01)------------------------- Fully completed the "Race/Prize Guide", finally. Everything is now completein the guide, save for a few things in the Arcade Mode branch of the FAQ.I might add this stuff later, but its doubtful.Version 0.57 (08/08/01)------------------------- Added the Car List Sorted by PriceVersion 0.55 (07/28/01)------------------------- Updated the Race/Prize List through Amatuer ModeVersion 0.50 (07/23/01)------------------------- Updated the " Ultimate Car Setups Guide" and updated the Race/Prize ListVersion 0.49 (07/20/01)------------------------- Updated the "Ultimate Car Setups Guide" and updated the Race/Prize List, aswell as the Arcade Mode section.Version 0.47 (07/18/01)------------------------- Updated the "Ultimate Car Setups Guide"Version 0.46 (07/17/01)------------------------- Updated the "Ultimate Car Setups Guide" and updated the Race/Prize ListVersion 0.45 (07/17/01)------------------------- Updated the "Ultimate Car Setups Guide" with 4 new car setups, and addedthe call down License Test guideVersion 0.40 (07/17/01)------------------------- Updated the "How To Win Fast Cash" section and the Race/Prize List

Reason Why Teens Use Drugs :: Drugs, Social Issues, Legal Issues

The reasons why teenagers enforce drugs.Drug use is the increasing problem among teenagers in todays High schools. Ever since the drug war of 1900, drugs have been a major problem in todays society. Use of drugs much(prenominal) as opium, morphine, and their derivatives were quite commonplace in nineteenth century America. While most students of contemporary naughty school drug education programs know about the use of coca leaves in early Coca-Cola and the opium trade with China, the matter of drug addiction at the vacate of the century is much more extensive than usually acknowledged. It is estimated that by 1975 there were somewhere in the neighborhood of 550,000 regular users of addictive drugs in the U.S. While this payoff may seem large (taking into consideration the smaller population of the country in 1970s as compared to today) it is actually surprisingly small even whensuch drugs were available over the counter. Cocaine, morphine, laudanum, and heroin were all availabl e in drug stores and through the mail. Until the Pure Food and Drug Act of 1906, banned the sale and dispersal of these such drugsIn local shops and stores ,and through the mail.Today, all of these drugs are illegalIncluding the most popular drug among teens and in the united states, marijuana.Most drug use begins in the preteen and teenage years, these years most crucial in the maturation process. During these years, teenagers are faced with difficulttasks of discovering their self identity, as well as their sexual roles,becoming independence, learning to cope with authority and searching for goals that would

A Case Study of a Colloid Cyst :: Medical Tumors Cancer Essays

A Case Study of a Colloid CystColloid cysts in the third ventricle of are very rare intracranial favorable tumors. The cysts are located deep inside which makes treatment of the tumor very difficult. It takes a team of skilled professionals to treat patient with these kinds of cysts. The two people that I will be focusing on are the Neurologist and the Neurosurgeon even though there are whole teams of people that specialize in neurosurgery and that see to the patients care pre and post operation. The Role of the NeurologistThe role of the Neurologist is to diagnose and come up with a plan of action mechanism for the patient, depending on what is misuse with the patient. The Neurologist can parliamentary law test for the patient to see what is wrong and what needs to be done. These tests can include but are not peculiar(a) to blood test, CT scan, or MRI scan. The Neurologist works with the Neurosurgeon and instructs him or her on what to do during the surgery. Patient can get referred to Neurologist for some(prenominal) reasons but most often the patient symptoms are intracranial pressure (headaches) and/ or dizzy spells. Common signs of a colloid cyst are short-term shop interruptions and papilledema. Papilledema is swelling of the optic disk where the optic nerve enters the eyeball. The optic nerve is responsible for carrying virtual impulses to the brain. Based on the symptoms of the patient the neurologist will order test to find out what is going on. A Magnetic Resonance Imaging (MRI) is one way of diagnosing. Most of the brain and central nervous musical arrangement problems are diagnosed through the use of a MRI. MRI creates an image using nuclear magnetic resonance and is possible because the human body is filled with picayune biological magnets. (See figure 1, normal brain during MRI) In the case of the patient he never saw a neurologist because his colloid cyst was found during a routine CAT scan following his accident. Howev er the patient was suffering from dizzy spells prior to surgery. For patients with a colloid cyst the most common plan of action is surgery, which is preformed about 93% of the time. The two method used most often are Transcallosal and transcortialtransventricular. Out of the 105 patients in the

Compensation Act 2006 Essay -- Negligence

Negligence as a tort is defined as a breach of a legal avocation to take carry off which results in damage to the claimant. It has been established that in order to raise liability and succeed in negligence claim, the claimant must show that the defendant owes him a duty of care, that this duty has been breached, and that he suffered damage or loss which is within the scope of the duty. However, the question of whether a breach of a duty of care has occurred, involves two elements how much care is required to be taken (in other words the standard of care) and whether that care has been taken. It is worth mentioning that the standard of care in negligence is objective , as held in Nettleship v Watson , in which the conduct of the defendant was examined. The situation, however, was not that clear. Under Caparo test , the courts will take into account in determining duty of care foreseeability of harm, proximity, and whether bossy a duty would be fair, just, and reasonable. Relat ively, it can be said that s.1 of the Compensation Acts 2006, revolves around similar principles of those mentioned in Caparo test. In fact, the courts are invited under section 1 (but not obliged) to take into account the impact of decisions they make on standard of care. Furthermore, in deciding whether the defendant have taken necessary steps to happen the standard of care, the courts are invited to examine whether those steps would prevent desirable activities from taking place, and discourage people from undertaking functions in connection with the activity. The question arises here, however, on whether settle had such discretion before the Act while deciding on standard of care. The answer lies in the explanatory notes of the Act, which declare... ...ckman 1990 2 ACMiller v. capital of Mississippi 1977 QB 966, CARobinson v Post Office 1974 1 WLR 1176Overseas Tankship (UK) Ltd v Miller Steamship Co Pty, The Wagon Mound (No 2 ) 1967 1 AC 617Nettleship v Weston 1971 2 QB Mc Hale v Watson 1966 CLR 199 Bolton v. Stone 1951 AC 850, HLDonoghue v Stevenson 1932 AC 562WebsiteEnd compensation culture Blair accessed seventh January 2011Compensation culture accessed 7th January 2011(Claire Mckenney), Questioning the claims culture (2004) accessed 7th January 2011Compensation Act 2006 Explanatory Notes accessed 7th January 2011

A Rasin in the Sun :: essays research papers

A seed is industrial kit and boodleed to begin a new, yet some quantifys on the way to becoming a bright, pulchritudinous plant, the plant lacks minerals or sunlight or water and is misshapen, much analogous that of a family? the Younger family, to be exact. Few gardeners will spend their precious time to help a sickly plant, knowing it will never bloom, to grow into nothing more than it already is. Yet, there are those exceptional ones? ?Mama? is, indeed, the closely tender of hearts to care for this sickly family that, I have no doubt, she knows will never fully blossom into a big, strong, and powerful family. The physical plant she cares for is a symbol for her family in every way. The mother waters the plant every chance she has, as illustrated on page 52. The ability for the mother to rationalise all else and cater to this plant can be said, too, about her family. The money, which comes in from her dead husband?s insurance is to be put towards what her family needs, not wh at she would like to have, what she would wish to have, no, the money is put towards her family?s future. She even tries to protect their pure hearts when she mentions, ?Now don?t act silly? We ain?t never been no people to act silly ?bout no money (68).? Protecting the family from greed, the root of all evil, is the main focus for this gardener of life, well(p) as she would protect the ravished plant from an overwhelming beam of sunlight. Placing a rod behind a plant is sometimes the best way to straighten a plant?s stem, yet the gnarled plant she cares for is still disfigured, as to is Walter. Mama tells Walter, ? It?s dangerous When a man goes outside his home to look for tranquillity (73),? in order to straighten his mind out, even though it doesn?t work out all fine and dandy, the effort is made. Without this gardener?s protection, the plant would have been evaporated, long ago, by the insanity that comes with the struggles of everyday life. Checking to see that the soil stil l has water, Mama makes sure that the family is not in danger of losing their love for from each one other, their power source for striving in the retched world, as if checking the soil on page 39 and then replenishing it by saying, ?

A Rasin in the Sun :: essays research papers

A seed is planted to begin a new, yet sometimes on the direction to comely a bright, beautiful plant, the plant lacks minerals or sunlight or water and is misshapen, much like that of a family? the Younger family, to be exact. Few nurserymans will overhaul their precious time to help a sickly plant, knowing it will never bloom, to grow into nothing more than it already is. Yet, there are those transcendent ones? ?Mama? is, indeed, the most tender of hearts to care for this sickly family that, I have no doubt, she knows will never fully blossom into a big, strong, and compelling family. The physical plant she cares for is a symbol for her family in every way. The mother waters the plant every chance she has, as illustrated on page 52. The energy for the mother to ignore all else and cater to this plant can be said, too, about her family. The money, which comes in from her dead husband?s insurance is to be endow towards what her family needs, not what she would like to have, wh at she would wish to have, no, the money is put towards her family?s future. She even tries to protect their pure hearts when she mentions, ?Now don?t act dotty? We ain?t never been no people to act silly ?bout no money (68).? Protecting the family from greed, the root of all evil, is the main centralize for this gardener of life, just as she would protect the ravished plant from an overwhelming beam of sunlight. Placing a rod behind a plant is sometimes the best way to straighten a plant?s stem, yet the gnarled plant she cares for is still disfigured, as to is Walter. Mama tells Walter, ? It?s perilous When a man goes outside his mansion to look for peace (73),? in order to straighten his mind out, even though it doesn?t work out all fine and dandy, the effort is made. Without this gardener?s protection, the plant would have been evaporated, long ago, by the insanity that comes with the struggles of everyday life. Checking to see that the soil still has water, Mama makes sure th at the family is not in danger of losing their love for each other, their power source for striving in the retched world, as if checking the soil on page 39 and then replenishing it by saying, ?

Populism DBQ

In the late nineteenth century, around 1880-1900, many farmers were experiencing problems and threats to their way of life. The valid complaints of the farmers dealt with the cash supply system in America and the large railroad companies. In 1892, the platform for the Populist Party was laid down. In this platform it is stated that the study power to create property is appropriated to enrich bondholders thereby adding millions to the burdens of the people.This is discussing the demonetization of eloquent and the negative effect it has on the common people, such as farmers. Later on in the platform is it also discussed that silver has had widespread acceptance as a coin for a very long time and by demonetizing it to change magnitude the purchasing power of gold, the impressions are several negative consequences which will flushtually lead to terrible convulsions, the destruction of civilization, or the establishment of an absolute despotism.This unhappiness of farmers regardin g the specie system in the United States is also shown in a political cartoon from The Farmers Voice, a Chicago newspaper in the late 1880s or betimes 1890s. The cartoon entitled The Eastern Master and His Western Slaves depicts farmers as slaves to the wealthy eastern businessmen. It is representing the exploitation of the farmers and shows yet another of their economic struggles the mortgages they bore on their farms.Further usher that supports and validates the farmers complaints about the current economic situation is found in William McKinleys acceptance speech given in Canton, Ohio on August 26, 1896. In his speech, McKinley said that even though free silver would not make farming less laborious and more profitable.. farmers and laborers are the ones who suffer the greatest as a result of the cheap money. They are the first to feel its bad do and the last to recover from them . The belief that silver is the solution of the problems for farmers is oppose in J.Laurence Lau ghlins Causes of Agricultural fermentation article in the November, 1896 issue of Atlicantic Monthly. Laughlin describes that the increase in supply without an increase of demand led farmers to believe that silver can solve their issues by his saying, the sudden enlargement of the supply without any corresponding increase of demand produced that alarming fall in the price of wheat which has been made the farmers excuse for thinking that silver is the magic panacea for all his illsHe then goes on to describe that farmers have simply pushed the blame on the scarcity of gold as opposed to realizing the actual cause is their own overproduction of wheat. The effects of the different acts and laws regarding money supply is shown in the United States government data from 1961 depicting the population of the res publica along side the money in circulation from the year 1865 through 1895. This data shows that from 1865 through 1885, the population was increasing, however the amount of mone y in circulation was decreasing rapidly.This suggests that the effects of the acts and laws regarding money were resulting in the deflation of of the currency. against the railroad companies is credible because during this time period the government showed enormous favoritism towards large businesses even though the railroads were monopolies. Further discontent with monopolies is verbalised in A Call to Action An Interpretation of the Great Uprising. Its Source and Causes by James B.Weaver, a Populist candidate for president in the preference of 1892. Weaver described that trusts and monopolies use threats, intimidation, bribery, fraud, wreck, and pillage to impoverish the producer, drive him to a single market, reduce the price of every class of labor connected with the trade, oblige out of employment large numbers of people , and finally they increase the price to the consumer .The farmers and laborers of the late nineteenth century faced two main problems money supply and la rge businesses such as railroads. These issues resulted in a variety of complaints from the agriculturists, however the grievances did prove to be valid based on the evidence previously presented. The farmers were struggling to survive off of the small profit they received, and they suffered even further when large monopolies and railroad companies took actions that dwindled their profits further.

Technological Development

The primary purpose off gas mask is to prevent deadly gases or poisonous tangible from accessing the lungs and attacking the person. It does not deliver its own oxygen supply, but cleans out the particles. They can also shield the face from any interaction poisons or gases. motorcar GunsUntil the machine gun was created, we only had rifles which was slow. You could only shoot one bullet at a time and then you had to load another bullet into the chamber using the bolt. When you calld the bolt, it would refuse the consumed container shell and load the next one into the chamber. This had to be done for every shot fired, and was cumbersome and took time. The machine gun is designed to shoot continuously hundreds of ammunitions per minute. The outcome it had been was to execute a lot of soldiers and far more than ever could be killed by soldiers equipped with rifles.An additional thing that helper making the machine gun so effective was with the means the soldiers were trained to br ing attack on the target. All of them would run toward the waves. This was in effect nub when the adversary was only armed with rifles where you had to aim to shoot the enemy. But, when an army of running men encountered machine guns they were trimmed down effortlessly. These particular firepower might even read even played a part of bringing the war to an end. Telephone The aim of the harmonic telegraph is to perceptibly connect with people ho ar without reach straightaway.Before the telephone, long distance messages were through telegraph machines that were less efficient and took longer to get messages across because only dots and dashes at the time could be communicated. Telephones atomic number 18 predominantly used for fast communication and crises. The vital thing to checkup survival in many bad conditions is for the wounded person to get medical assistance quickly. Back then before when the telephone wasnt invented, it was hard for people to get assistance quickly. Tele phones allow for rapid immunization during normal catastrophes as well.That way it is likely for the government to give warning to people of imminent disaster before they come. The telephone also has the function of easy conversation in fairish days circumstances like calling to order items such as pies, cabs, pizza or flowers. People may talk in an ordinary way to hold forth their lives Just as they would if they were at the same places. Radio radio came out when televisions didnt exist, but it was used in a similar function as we use a TV currently, like universe attentive to comedy shows and music.When the TV was created, the radio lost its fame and popularity as a result of folks favored the TV more. However, radios were convenient that they became mainly used primarily for music shows and speaking. And, that their purpose nowadays. In todays world radio is free and this is a good thing because we are normally paying for some sort of entertainment. Moreover, if you are drivin g you can turn on the radio and get updated news on traffic Jams or being on the lookout and music that you can enjoy while driving.

Open Domain Event Extraction from Twitter

Open Domain Event Extraction from chirp Alan Ritter University of Washington electronic computer Sci. & Eng. Seattle, WA emailprotected washington. edu Mausam University of Washington Computer Sci. & Eng. Seattle, WA emailprotected washington. edu Oren Etzioni University of Washington Computer Sci. & Eng. Seattle, WA emailprotected washington. edu Sam Clark? Decide, Inc. Seattle, WA sclark. emailprotected com ABSTRACT Tweets argon the roughly up-to- picture and inclusive stream of selective instruction and commentary on accr alter compositors cases, but they argon in tote upition fragmented and noisy, motivating the need for systems that can extract, aggregate and categorize main(prenominal) issuances. preceding(prenominal) lead on extracting structured representations of features has concentrate largely on newswire text chitters whimsical characteristics present new challenges and opportunities for sluttish-domain return extraction. This paper describes TwiCal the ? rst open-domain vitrine-extraction and categorization system for Twitter. We demonstrate that accurately extracting an open-domain schedule of signi? pharisaism essences from Twitter is indeed feasible. In addition, we present a novel draw near for disc overing important event categories and classifying extracted events based on latent variable models.By leverage large volumes of unlabeled information, our address achieves a 14% increase in maximal F1 over a administer service line. A continuously updating ostensorium of our system can be viewed at http//statu outmatchndar. com Our NLP tools ar available at http//github. com/aritter/ twitter_nlp. Entity Steve Jobs iPhone GOP Amanda Knox Event Phrase died announcement debate verdict Date 10/6/11 10/4/11 9/7/11 10/3/11 Type Death ProductLaunch PoliticalEvent Trial elude 1 Examples of events extracted by TwiCal. vents. Yet the number of tweets posted daily has recently exceeded two-hundred million, many of which be eit her redundant 57, or of limited interest, leading to information overload. 1 Clearly, we can bene? t from more(prenominal) structured representations of events that atomic number 18 synthesized from individual tweets. Previous shape in event extraction 21, 1, 54, 18, 43, 11, 7 has focuse largely on news articles, as historically this genre of text has been the best source of information on current events.Read withal Twitter Case StudyIn the meantime, sociable net functional sites such(prenominal) as Facebook and Twitter have become an important complementary source of such information. While status messages consist a wealth of utilitarian information, they are actually disorganized motivating the need for automatic extraction, aggregation and categorization. Although on that point has been very much interest in introduce trends or memes in cordial media 26, 29, little represent has addressed the challenges arising from extracting structured representations of events from short or informal texts.Extracting useful structured representations of events from this disorganized corpus of noisy text is a ambitious problem. On the other hand, individual tweets are short and self-contained and are therefore not composed of complex discourse structure as is the courting for texts containing narratives. In this paper we demonstrate that open-domain event extraction from Twitter is indeed feasible, for guinea pig our highest-con? dence extracted future events are 90% accurate as demonstrated in 8.Twitter has several characteristics which present unique challenges and opportunities for the task of open-domain event extraction. Challenges Twitter users frequently mention mundane events in their daily lives (such as what they ate for lunch) which are wholly of interest to their immediate social net last. In contrast, if an event is mentioned in newswire text, it 1 http//blog. twitter. com/2011/06/ 200-million-tweets-per-day. html Categories and Subject Descrip tors I. 2. 7 Natural Language Processing Language parsing and understanding H. 2. Database Management Database application programsdata archeological site General Terms Algorithms, Experimentation 1. INTRODUCTION Social networking sites such as Facebook and Twitter present the most up-to- catch information and buzz ab place current ? This work was conducted at the University of Washington Permission to make digital or hard copies of all or part of this work for personal or schoolroom use is dedicateed without fee provided that copies are not made or distributed for pro? t or commercial advantage and that copies bear this notice and the intact citation on the ? rst page.To copy otherwise, to republish, to post on servers or to redistribute to lists, asks prior speci? c permission and/or a fee. KDD12, August 1216, 2012, Beijing, China. secure 2012 ACM 978-1-4503-1462-6 /12/08 $10. 00. is safe to assume it is of command importance. Individual tweets are to a fault very terse, often lacking su? cient context to categorize them into topics of interest (e. g. Sports, Politics, Product unload etc ). Further because Twitter users can talk about whatever they choose, it is unclear in advance which set of event types are get hold of. ultimately, tweets are written in an informal appearance ca development NLP tools designed for edited texts to perform exceedingly poorly. Opportunities The short and self-contained nature of tweets means they have very simple discourse and prosaic structure, issues which still challenge state-of-the-art NLP systems. For showcase in newswire, complex reasoning about sexual congresss surrounded by events (e. g. before and after ) is often postulate to accurately relate events to temporal expressions 32, 8. The volume of Tweets is as well as much larger than the volume of news articles, so redundancy of information can be utilise more easily.To address Twitters noisy ardour, we follow recent work on NLP in noisy text 46, 3 1, 19, annotating a corpus of Tweets with events, which is because used as training data for episode-labeling models to identify event mentions in millions of messages. Because of the terse, sometimes mundane, but highly redundant nature of tweets, we were prompt to focus on extracting an aggregate representation of events which provides additional context for tasks such as event categorization, and also ? lters out mundane events by exploiting redundancy of information.We train identifying important events as those whose mentions are strongly associated with references to a unique date as opposed to dates which are evenly distributed across the calendar. Twitter users discuss a wide variety of topics, making it unclear in advance what set of event types are attach for categorization. To address the diversity of events discussed on Twitter, we introduce a novel approach to discovering important event types and categorizing aggregate events inwardly a new domain. Supervised or semi-supervised approaches to event categorization would require ? st designing government note guidelines (including selecting an appropriate set of types to compose), then annotating a large corpus of events found in Twitter. This approach has several drawbacks, as it is apriori unclear what set of types should be annotated a large amount of e? ort would be required to manually annotate a corpus of events while simultaneously re? ning annotation standards. We volunteer an approach to open-domain event categorization based on latent variable models that uncovers an appropriate set of types which match the data.The automatically detect types are subsequently inspected to ? lter out any which are incoherent and the rest are annotated with informative labels2 examples of types discovered using our approach are listed in ? gure 3. The resulting set of types are then applied to categorize hundreds of millions of extracted events without the use of any manually annotated examples. By leveraging large quantities of unlabeled data, our approach results in a 14% improvement in F1 score over a supervised baseline which uses the same set of types. Stanford NER T-seg P 0. 62 0. 73 R 0. 5 0. 61 F1 0. 44 0. 67 F1 inc. 52% hold over 2 By training on in-domain data, we obtain a 52% improvement in F1 score over the Stanford Named Entity Recognizer at segmenting entities in Tweets 46. 2. SYSTEM OVERVIEW TwiCal extracts a 4-tuple representation of events which includes a named entity, event phrase, calendar date, and event type (see gameboard 1). This representation was chosen to closely match the way important events are typically mentioned in Twitter. An overview of the various components of our system for extracting events from Twitter is presented in Figure 1.Given a raw stream of tweets, our system extracts named entities in association with event phrases and unambiguous dates which are involved in signi? cant events. First the tweets are POS tagged, then named enti ties and event phrases are extracted, temporal expressions resolved, and the extracted events are categorized into types. Finally we judge the strength of association between individually named entity and date based on the number of tweets they co-occur in, in ordination to determine whether an event is signi? cant.NLP tools, such as named entity segmenters and part of speech taggers which were designed to process edited texts (e. g. news articles) perform very poorly when applied to Twitter text due to its noisy and unique style. To address these issues, we utilize a named entity tagger and part of speech tagger dexterous on in-domain Twitter data presented in previous work 46. We also develop an event tagger skilful on in-domain annotated data as described in 4. 3. NAMED ENTITY SEGMENTATION NLP tools, such as named entity segmenters and part of speech taggers which were designed to process edited texts (e. g. ews articles) perform very poorly when applied to Twitter text due t o its noisy and unique style. For instance, capitalization is a key feature for named entity extraction within news, but this feature is highly unreliable in tweets words are often capitalized simply for emphasis, and named entities are often left all lowercase. In addition, tweets contain a higher proportion of out-ofvocabulary words, due to Twitters 140 character limit and the creative spelling of its users. To address these issues, we utilize a named entity tagger trained on in-domain Twitter data presented in previous work 46. Training on tweets vastly improves exertion at segmenting Named Entities. For example, performance compared against the state-of-the-art news-trained Stanford Named Entity Recognizer 17 is presented in Table 2. Our system obtains a 52% increase in F1 score over the Stanford Tagger at segmenting named entities. 4. EXTRACTING EVENT MENTIONS This annotation and ? ltering takes minimal e? ort. One of the authors spent nigh 30 minutes inspecting and annotati ng the automatically discovered event types. 2 In rove to extract event mentions from Twitters noisy text, we ? st annotate a corpus of tweets, which is then 3 Available at http//github. com/aritter/twitter_nlp. Temporal Resolution S M T W T F S Tweets POS Tag NER Signi? cance Ranking Calendar Entries Event Tagger Event Classi? cation Figure 1 Processing pipeline for extracting events from Twitter. New components developed as part of this work are shaded in grey. used to train sequence models to extract events. While we apply an established approach to sequence-labeling tasks in noisy text 46, 31, 19, this is the ? rst work to extract eventreferring phrases in Twitter.Event phrases can consist of many di? erent move of speech as illustrated in the following examples Verbs Apple to Announce iPhone 5 on October 4th? YES Nouns iPhone 5 announcement coming Oct 4th Adjectives WOOOHOO NEW IPHONE TODAY CANT WAIT These phrases provide important context, for example extracting the entit y, Steve Jobs and the event phrase died in connection with October 5th, is much more informative than simply extracting Steve Jobs. In addition, event mentions are helpful in upstream tasks such as categorizing events into types, as described in 6.In order to build a tagger for recognizing events, we annotated 1,000 tweets (19,484 tokens) with event phrases, following annotation guidelines similar to those developed for the Event tags in Timebank 43. We treat the problem of recognizing event triggers as a sequence labeling task, using Conditional Random Fields for learning and conclusion 24. Linear Chain CRFs model dependencies between the predicted labels of adjacent words, which is bene? cial for extracting multi-word event phrases.We use contextual, dictionary, and orthographic features, and also include features based on our Twitter-tuned POS tagger 46, and dictionaries of event terms gathered from WordNet by Sauri et al. 50. The preciseness and recall at segmenting event phras es are describe in Table 3. Our classi? er, TwiCal-Event, obtains an F-score of 0. 64. To demonstrate the need for in-domain training data, we compare against a baseline of training our system on the Timebank corpus. precision 0. 56 0. 48 0. 24 recall 0. 74 0. 70 0. 11 F1 0. 64 0. 57 0. 15 TwiCal-Event No POS TimebankTable 3 Precision and recall at event phrase extraction. All results are reported using 4-fold cross validation over the 1,000 manually annotated tweets (about 19K tokens). We compare against a system which doesnt make use of features generated based on our Twitter trained POS Tagger, in addition to a system trained on the Timebank corpus which uses the same set of features. as input a reference date, some text, and parts of speech (from our Twitter-trained POS tagger) and marks temporal expressions with unambiguous calendar references. Although this mostly rule-based system was designed for use on newswire text, we ? d its precision on Tweets (94% mindd over as hav e of 268 extractions) is su? ciently high to be useful for our purposes. TempExs high precision on Tweets can be explained by the fact that some temporal expressions are relatively unambiguous. Although there appears to be room for improving the recall of temporal extraction on Twitter by handling noisy temporal expressions (for example see Ritter et. al. 46 for a list of over 50 spelling variations on the word tomorrow), we leave adapting temporal extraction to Twitter as authority future work. . CLASSIFICATION OF EVENT TYPES To categorize the extracted events into types we propose an approach based on latent variable models which infers an appropriate set of event types to match our data, and also classi? es events into types by leveraging large amounts of unlabeled data. Supervised or semi-supervised classi? cation of event categories is problematic for a number of reasons. First, it is a priori unclear which categories are appropriate for Twitter. Secondly, a large amount of ma nual e? ort is required to annotate tweets with event types.Third, the set of important categories (and entities) is belike to shift over time, or within a focused user demographic. Finally many important categories are relatively infrequent, so even a large annotated dataset may contain just a few examples of these categories, making classi? cation di? cult. For these reasons we were motivated to investigate un- 5. EXTRACTING AND RESOLVING TEMPORAL EXPRESSIONS In addition to extracting events and think named entities, we also need to extract when they occur. In general there are many di? rent ways users can refer to the same calendar date, for example next Friday, August 12th, tomorrow or yesterday could all refer to the same day, depending on when the tweet was written. To resolve temporal expressions we make use of TempEx 33, which takes Sports Party TV Politics Celebrity music Movie Food Concert mathematical operation Fitness Interview ProductRelease Meeting Fashion Finance School AlbumRelease pietism 7. 45% 3. 66% 3. 04% 2. 92% 2. 38% 1. 96% 1. 92% 1. 87% 1. 53% 1. 42% 1. 11% 1. 01% 0. 95% 0. 88% 0. 87% 0. 85% 0. 85% 0. 78% 0. 71% Con? ct Prize Legal Death Sale VideoGameRelease Graduation Racing Fundraiser/Drive Exhibit Celeb ration Books Film Opening/Closing Wedding Holiday medical Wrestling OTHER 0. 69% 0. 68% 0. 67% 0. 66% 0. 66% 0. 65% 0. 63% 0. 61% 0. 60% 0. 60% 0. 60% 0. 58% 0. 50% 0. 49% 0. 46% 0. 45% 0. 42% 0. 41% 53. 45% Label Sports Concert Perform TV Movie Sports Politics Figure 2 pure(a) list of automatically discovered event types with percentage of data covered. Interpretable types representing signi? cant events cover roughly half of the data. supervised approaches that will automatically gravel event types which match the data.We adopt an approach based on latent variable models inspired by recent work on modeling selectional preferences 47, 39, 22, 52, 48, and unattended information extraction 4, 55, 7. Each event indicator phras e in our data, e, is modeled as a mixture of types. For example the event phrase cheered aptitude appear as part of either a PoliticalEvent, or a SportsEvent. Each type corresponds to a dispersion over named entities n involved in speci? c instances of the type, in addition to a distribution over dates d on which events of the type occur. Including calendar dates in our model has the e? ct of support (though not requiring) events which occur on the same date to be assigned the same type. This is helpful in guiding inference, because distinct references to the same event should also have the same type. The fertile story for our data is based on LinkLDA 15, and is presented as Algorithm 1. This approach has the advantage that information about an event phrases type distribution is shared across its mentions, while ambiguity is also naturally preserved. In addition, because the approach is based on generative a probabilistic model, it is straightforward to perform many di? rent pro babilistic queries about the data. This is useful for example when categorizing aggregate events. For inference we use collapsed Gibbs Sampling 20 where severally out of sight variable, zi , is sampled in turn, and parameters are integrated out. Example types are displayed in Figure 3. To estimate the distribution over types for a giftn event, a sample of the corresponding hidden variables is taken from the Gibbs markov chain after su? cient burn in. Prediction for new data is performed using a streaming approach to inference 56. TV Product MeetingTop 5 Event Phrases tailgate scrimmage tailgating homecoming regular anneal concert presale performs concerts tickets matinee musical priscilla seeing wicked new season season ? nale ? nished season episodes new episode watch love dialogue theme inception hall pass movie inning innings pitched homered homer presidential debate osama presidential candidate republican debate debate performance network news broadcast airing primetime drama channel stream unveils uncover announces launches wraps o? shows trading hall mtg zoning brie? g stocks tumbled trading report opened higher tumbles maths english test exam revise physics in stores album out debut album drops on hits stores voted o? idol scotty idol season dividendpaying sermon preaching preached worship preach declare war war shelling opened ? re wounded senate legislation repeal budget election winners lotto results enter winner contest bond certificate plea murder trial sentenced plea convicted ? lm festival screening starring ? lm gosling live forever passed away sad news condolences burried add into 50% o? up shipping save up donate tornado relief disaster relief donated raise money Top 5 Entities espn ncaa tigers eagles varsity taylor swift toronto britney spears rihanna rock shrek les mis lee evans wicked broadway jersey shore true blood gleefulness dvr hbo net? ix bl ack swan baneful tron scott pilgrim mlb red sox yankees twins dl obama president obama gop cnn america nbc espn abc fox mtv apple google microsoft uk sony town hall city hall club commerce neat house reuters new york u. . china euro english maths german bio twitter itunes ep uk amazon cd lady gaga american idol america beyonce glee church jesus pastor faith god libya afghanistan syria syria nato senate house congress obama gop ipad award facebook good luck winners casey anthony court india new delhi supreme court Hollywood nyc la los angeles new york michael jackson afghanistan john lennon young peace groupon early bird facebook etsy etsy japan red cross joplin june africaFinance School Album TV Religion Con? ict Politics Prize Legal Movie Death Sale Drive 6. 1 Evaluation To evaluate the ability of our model to classify signi? cant events, we gathered 65 million extracted events of the form Figure 3 Example event typ es discovered by our model. For each type t, we list the top 5 entities which have highest probability given t, and the 5 event phrases which assign highest probability to t. Algorithm 1 Generative story for our data involving event types as hidden variables.Bayesian Inference techniques are applied to invert the generative process and infer an appropriate set of types to describe the observed events. for each event type t = 1 . . . T do n Generate ? t according to centrosymmetric Dirichlet distribution Dir(? n ). d Generate ? t according to symmetric Dirichlet distribution Dir(? d ). end for for each unique event phrase e = 1 . . . E do Generate ? e according to Dirichlet distribution Dir(? ). for each entity which co-occurs with e, i = 1 . . . Ne do n Generate ze,i from Multinomial(? e ). Generate the entity ne,i from Multinomial(? n ). e,i TwiCal-Classify Supervised Baseline Precision 0. 85 0. 61 Recall 0. 55 0. 57 F1 0. 67 0. 59 Table 4 Precision and recall of event type categ orization at the point of maximum F1 score. d,i end for end for 0. 6 end for for each date which co-occurs with e, i = 1 . . . Nd do d Generate ze,i from Multinomial(? e ). Generate the date de,i from Multinomial(? zn ). Precision 0. 8 1. 0 listed in Figure 1 (not including the type). We then ran Gibbs Sampling with 100 types for 1,000 iterations of burnin, keeping the hidden variable assignments found in the last sample. One of the authors manually inspected the resulting types and assigned them labels such as Sports, Politics, MusicRelease and so on, based on their distribution over entities, and the event words which assign highest probability to that type. Out of the 100 types, we found 52 to correspond to coherent event types which referred to signi? cant events5 the other types were either incoherent, or covered types of events which are not of general interest, for example there was a cluster of phrases such as applied, call, contact, job interview, etc hich correspond to use rs discussing events related to searching for a job. Such event types which do not correspond to signi? cant events of general interest were simply marked as OTHER. A complete list of labels used to annotate the automatically discovered event types a farsighted with the coverage of each type is listed in ? gure 2. Note that this assignment of labels to types only needs to be done once and produces a labeling for an haphazard large number of event instances. Additionally the same set of types can easily be used to lassify new event instances using streaming inference techniques 56. One interesting direction for future work is automatic labeling and coherence evaluation of automatically discovered event types analogous to recent work on topic models 38, 25. In order to evaluate the ability of our model to classify aggregate events, we grouped together all (entity,date) pairs which occur 20 or more times the data, then annotated the 500 with highest association (see 7) using the event types discovered by our model. To help demonstrate the bene? s of leveraging large quantities of unlabeled data for event classi? cation, we compare against a supervised Maximum Entropy baseline which makes use of the 500 annotated events using 10-fold cross validation. For features, we treat the set of event phrases To scale up to larger datasets, we performed inference in parallel on 40 cores using an approximation to the Gibbs Sampling procedure analogous to that presented by Newmann et. al. 37. 5 after labeling some types were combined resulting in 37 distinct labels. 4 0. 4 Supervised Baseline TwiCal? Classify 0. 0 0. 2 0. 4 Recall 0. 0. 8 Figure 4 types. Precision and recall predicting event that co-occur with each (entity, date) pair as a bag-of-words, and also include the associated entity. Because many event categories are infrequent, there are often few or no training examples for a category, leading to low performance. Figure 4 compares the performance of our unsupervise d approach to the supervised baseline, via a precision-recall curve obtained by varying the threshold on the probability of the most likely type. In addition table 4 compares precision and recall at the point of maximum F-score.Our unsupervised approach to event categorization achieves a 14% increase in maximum F1 score over the supervised baseline. Figure 5 plots the maximum F1 score as the amount of training data used by the baseline is varied. It seems likely that with more data, performance will reach that of our approach which does not make use of any annotated events, however our approach both automatically discovers an appropriate set of event types and provides an initial classi? er with minimal e? ort, making it useful as a ? rst step in situations where annotated data is not immediately available. . RANKING EVENTS Simply using frequency to determine which events are signi? cant is insu? cient, because many tweets refer to common events in users daily lives. As an example, users often mention what they are eating for lunch, therefore entities such as McDonalds occur relatively frequently in association with references to most calendar days. Important events can be severalise as those which have strong association with a unique date as opposed to being spread evenly across days on the calendar. To extract signi? ant events of general interest from Twitter, we thus need some way to measure the strength of association between an entity and a date. In order to measure the association strength between an 0. 8 0. 2 Supervised Baseline TwiCal? Classify 100 200 300 400 tweets. We then added the extracted triples to the dataset used for inferring event types described in 6, and performed 50 iterations of Gibbs sampling for predicting event types on the new data, holding the hidden variables in the original data constant. This streaming approach to inference is similar to that presented by Yao et al. 56. We then ranked the extracted events as described in 7, a nd randomly sampled 50 events from the top ranked 100, 500, and 1,000. We annotated the events with 4 separate criteria 1. Is there a signi? cant event involving the extracted entity which will take place on the extracted date? 2. Is the most frequently extracted event phrase informative? 3. Is the events type correctly classi? ed? 4. Are each of (1-3) correct? That is, does the event contain a correct entity, date, event phrase, and type? Note that if (1) is marked as incorrect for a speci? event, subsequent criteria are always marked incorrect. Max F1 0. 4 0. 6 Training Examples Figure 5 Maximum F1 score of the supervised baseline as the amount of training data is varied. entity and a speci? c date, we utilize the G log likelihood ratio statistic. G2 has been argued to be more appropriate for text analysis tasks than ? 2 12. Although Fishers Exact test would produce more accurate p-values 34, given the amount of data with which we are working (sample size great than 1011 ), it p roves di? cult to compute Fishers Exact Test Statistic, which results in ? ating point over? ow even when using 64-bit operations. The G2 test whole kit and boodle su? ciently well in our setting, however, as computing association between entities and dates produces less sparse contingency tables than when working with pairs of entities (or words). The G2 test is based on the likelihood ratio between a model in which the entity is conditioned on the date, and a model of independence between entities and date references. For a given entity e and date d this statistic can be computed as follows G2 = x? e,e,y? d,d 2 8. 2 BaselineTo demonstrate the importance of natural language processing and information extraction techniques in extracting informative events, we compare against a simple baseline which does not make use of the Ritter et. al. named entity recognizer or our event recognizer instead, it considers all 1-4 grams in each tweet as candidate calendar entries, relying on the G2 test to ? lter out phrases which have low association with each date. 8. 3 Results The results of the evaluation are displayed in table 5. The table shows the precision of the systems at di? rent yield levels (number of aggregate events). These are obtained by varying the thresholds in the G2 statistic. Note that the baseline is only equal to the third column, i. e. , the precision of (entity, date) pairs, since the baseline is not performing event identi? cation and classi? cation. Although in some cases ngrams do correspond to informative calendar entries, the precision of the ngram baseline is extremely low compared with our system. In many cases the ngrams dont correspond to salient entities related to events they often consist of single words which are di? ult to interpret, for example prisonbreak which is part of the movie Twilight Breaking Dawn vacated on November 18. Although the word Breaking has a strong association with November 18, by itself it is not very informative to present to a user. 7 Our high-con? dence calendar entries are surprisingly high quality. If we limit the data to the 100 highest ranked calendar entries over a two-week date range in the future, the precision of extracted (entity, date) pairs is quite good (90%) an 80% increase over the ngram baseline.As expected precision drops as more calendar entries are displayed, but 7 In addition, we notice that the ngram baseline tends to produce many near-duplicate calendar entries, for example Twilight Breaking, Breaking Dawn, and Twilight Breaking Dawn. While each of these entries was annotated as correct, it would be problematic to show this many entries describing the same event to a user. Ox,y ? ln Ox,y Ex,y Where Oe,d is the observed fraction of tweets containing both e and d, Oe,d is the observed fraction of tweets containing e, but not d, and so on.Similarly Ee,d is the expected fraction of tweets containing both e and d assuming a model of independence. 8. EXPERIMENTS To estima te the quality of the calendar entries generated using our approach we manually evaluated a sample of the top 100, 500 and 1,000 calendar entries occurring within a 2-week future window of November 3rd. 8. 1 Data For evaluation purposes, we gathered roughly the 100 million most recent tweets on November 3rd 2011 (collected using the Twitter Streaming API6 , and tracking a broad set of temporal keywords, including today, tomorrow, names of weekdays, months, etc. ).We extracted named entities in addition to event phrases, and temporal expressions from the text of each of the 100M 6 https//dev. twitter. com/docs/streaming-api Mon Nov 7 Justin meet another(prenominal) Motorola Pro+ kick Product Release Nook Color 2 launch Product Release Eid-ul-Azha celebrated Performance MW3 midnight release Other Tue Nov 8 Paris love Other iPhone holding Product Release Election Day vote Political Event Blue err Park listening Music Release Hedley album Music Release Wed Nov 9 EAS test Other The Fed s cut o? Other Toca Rivera promoted Performance Alert System test Other Max Day give OtherNovember 2011 Thu Nov 10 Fri Nov 11 Robert Pattinson iPhone show debut Performance Product Release jam Murdoch Remembrance Day give evidence open Other Performance RTL-TVI France post play TV Event Other Gotti Live Veterans Day work closed Other Other Bambi Awards Skyrim perform arrives Performance Product Release Sat Nov 12 Sydney perform Other Pullman Ballroom promoted Other Fox ? ght Other Plaza party Party Red Carpet invited Party Sun Nov 13 Playstation answers Product Release Samsung Galaxy Tab launch Product Release Sony answers Product Release Chibi Chibi Burger other Jiexpo Kemayoran promoted TV EventFigure 6 Example future calendar entries extracted by our system for the week of November 7th. Data was collected up to November 5th. For each day, we list the top 5 events including the entity, event phrase, and event type. While there are several errors, the majority of calendar entries are informative, for example the Muslim vacation eid-ul-azha, the release of several videogames Modern Warfare 3 (MW3) and Skyrim, in addition to the release of the new playstation 3D display on Nov 13th, and the new iPhone 4S in Hong Kong on Nov 11th. calendar entries 100 500 1,000 ngram baseline 0. 50 0. 6 0. 44 entity + date 0. 90 0. 66 0. 52 precision event phrase event 0. 86 0. 56 0. 42 type 0. 72 0. 54 0. 40 entity + date + event + type 0. 70 0. 42 0. 32 Table 5 Evaluation of precision at di? erent recall levels (generated by varying the threshold of the G2 statistic). We evaluate the top 100, 500 and 1,000 (entity, date) pairs. In addition we evaluate the precision of the most frequently extracted event phrase, and the predicted event type in association with these calendar entries. Also listed is the fraction of cases where all predictions (entity + date + event + type) are correct.We also compare against the precision of a simple ngram baseline which does not make use of o ur NLP tools. Note that the ngram baseline is only comparable to the entity+date precision (column 3) since it does not include event phrases or types. remains high enough to display to users (in a ranked list). In addition to being less likely to come from extraction errors, highly ranked entity/date pairs are more likely to relate to popular or important events, and are therefore of greater interest to users. In addition we present a sample of extracted future events on a calendar in ? ure 6 in order to give an example of how they might be presented to a user. We present the top 5 entities associated with each date, in addition to the most frequently extracted event phrase, and highest probability event type. 9. RELATED WORK While we are the ? rst to study open domain event extraction within Twitter, there are two key related strands of research extracting speci? c types of events from Twitter, and extracting open-domain events from news 43. Recently there has been much interest i n information extraction and event identi? cation within Twitter. Benson et al. 5 use distant superintendence to train a relation extractor which identi? es artists and venues mentioned within tweets of users who list their location as New York City. Sakaki et al. 49 train a classi? er to recognize tweets reporting earthquakes in Japan they demonstrate their system is capable of recognizing almost all earthquakes reported by the Japan Meteorological Agency. Additionally there is recent work on detecting events or tracking topics 29 in Twitter which does not extract structured representations, but has the advantage that it is not limited to a peg domain. Petrovi? t al. investigate a streaming approach to identic fying Tweets which are the ? rst to report a breaking news story using Locally Sensitive hashish Functions 40. Becker et al. 3, Popescu et al. 42, 41 and Lin et al. 28 investigate discovering clusters of related words or tweets which correspond to events in progress. In c ontrast to previous work on Twitter event identi? cation, our approach is main(a) of event type or domain and is thus more widely applicable. Additionally, our work focuses on extracting a calendar of events (including those occurring in the future), extract- . 4 Error abridgment We found 2 main causes for why entity/date pairs were uninformative for display on a calendar, which occur in roughly equal proportion partitioning Errors Some extracted entities or ngrams dont correspond to named entities or are generally uninformative because they are mis-segmented. Examples include RSVP, Breaking and Yikes. Weak Association between Entity and Date In some cases, entities are properly segmented, but are uninformative because they are not strongly associated with a speci? c event on the associated date, or are involved in many di? rent events which happen to occur on that day. Examples include locations such as New York, and frequently mentioned entities, such as Twitter. ing event-refe rring expressions and categorizing events into types. Also relevant is work on identifying events 23, 10, 6, and extracting timelines 30 from news articles. 8 Twitter status messages present both unique challenges and opportunities when compared with news articles. Twitters noisy text presents serious challenges for NLP tools. On the other hand, it contains a higher proportion of references to present and future dates.Tweets do not require complex reasoning about relations between events in order to place them on a timeline as is typically necessary in long texts containing narratives 51. Additionally, unlike News, Tweets often discus mundane events which are not of general interest, so it is crucial to exploit redundancy of information to assess whether an event is signi? cant. Previous work on open-domain information extraction 2, 53, 16 has mostly focused on extracting relations (as opposed to events) from web corpora and has also extracted relations based on verbs.In contrast, t his work extracts events, using tools adapted to Twitters noisy text, and extracts event phrases which are often adjectives or nouns, for example Super Bowl Party on Feb 5th. Finally we note that there has recently been increasing interest in applying NLP techniques to short informal messages such as those found on Twitter. For example, recent work has explored Part of Speech tagging 19, geographical variation in language found on Twitter 13, 14, modeling informal conversations 44, 45, 9, and also applying NLP techniques to help crisis workers with the ? ood of information following natural disasters 35, 27, 36. 1. ACKNOWLEDGEMENTS The authors would like to thank Luke Zettlemoyer and the anonymous reviewers for helpful feedback on a previous draft. This research was supported in part by NSF grant IIS-0803481 and ONR grant N00014-08-1-0431 and carried out at the University of Washingtons Turing Center. 12. REFERENCES 1 J. Allan, R. Papka, and V. Lavrenko. On-line new event detection and tracking. In SIGIR, 1998. 2 M. Banko, M. J. Cafarella, S. Soderl, M. Broadhead, and O. Etzioni. Open information extraction from the web. In In IJCAI, 2007. 3 H. Becker, M. Naaman, and L. Gravano. Beyond trending topics Real-world event identi? ation on twitter. In ICWSM, 2011. 4 C. Bejan, M. Titsworth, A. Hickl, and S. 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A. Smith. Part-of-speech tagging 10. CONCLUSIONS We have presented a climbable and open-domain approach to extracting and categorizing events from status messages. We evaluated the quality of these events in a manual evaluation showing a clear improvement in performance over an ngram baseline We proposed a novel approach to categorizing events in an open-domain text genre with unknown types.Our approach based on latent variable models ? rst discovers event types which match the data, which are then used to classify aggregate events without any annotated examples. Because this approach is able to leverage large quantities of unlabeled data, it outperforms a supervised baseline by 14%. A possible avenue for future work is extraction of even richer event representations, while maintaining domain independence. 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In Proceedings of the 21 st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR 98, 1998. 55 L. Yao, A. Haghighi, S. Riedel, and A. McCallum. Structured relation discovery using generative models. In EMNLP, 2011. 56 L. Yao, D. Mimno, and A. McCallum. E? cient methods for topic model inference on streaming document collections. In KDD, 2009. 57 F. M. Zanzotto, M. Pennaccchiotti, and K. Tsioutsiouliklis. Linguistic redundancy in twitter. In EMNLP, 2011.

Supernatural machinery of Rape of the Lock

British working-class bowel movement for parliamentaryreformnamed after thePeoples Charter, a bill drafted by theLondonradicalWilliamLovettin May 1838. It contained six demands universal manhoodsuffrage, equal electoral districts, vote by ballot, one-yearly elected fantans, payment of members ofParliament, and abolition of the property qualifications for membership. Chartism was the first movement both working class in character and national in kitchen range that grew out of the protest against the injustices of the new industrial and political order in Britain.While composed of working people, Chartism was also mobilized around populism as ell as clan identity. lmagesThe movement was born amid the economic depression of 1837-38, when high unemploymentand the effects of the Poor Law Amendment Act of 1834 were felt in all parts of Britain. Lovetts charter provided a political platform acceptable to a heterogeneous working-class population. The movement swelled to national importan ce under the vigorous leadership of the IrishmanFeargus Edward OConnor, who stumped the nation in 1838 in second of the six points.While some of the massive Irish presence in Britain supported Chartism, most were devoted to the Catholic Repeal movement ofDaniel OConnell. A Chartist shape met in London in February 1839 to prepare apetitionto present to Parliament. Ulterior measures were threatened should Parliament ignore the demands, but the delegates differed in their degrees of combativeness and over whatform ulterior measures should take. In May the convention moved to Birmingham, where riots led to the arrest of its moderate leaders Lovett and John Collins.The rump of the convention returned to London and presented its petition in July. Parliament rejected it summarily. There followed in Novemberan armed rising of the physical force Chartists atNewport, which was quickly suppressed. Its principal leaders were banished toAustralia, and nearly every other Chartist leader was arr ested andsentenced to a short prison term. The Chartists then started to emphasize efficient organization and moderate tactics.Three years later a second national petition was presented containing more than troika million signatures, but again Parliamentrefused to consider it. The movement lost some oflts mass support later in the 1840s as the economy revived. Also, the movement to lift theCorn Lawsdivided radical energies, and severaldiscouraged Chartist leaders turned to other projects. The last great burst of Chartism occurred in 848. Another convention was summoned, and another petition was prepared. Again Parliament did nothing.Thereafter, Chartism lingered another decade in the provinces, but its appeal as a national mass movement was ended. With the onset of the relative prosperity of mid-Victorian Britain, popular militancy lost its edge. Many Chartist leaders, however, schooled in the ideological debates of the 1840s, continued to serve popular causes, and the Chartist sp irit outlasted the organization. Five of the six pointsall except the annual Parliamentshave since been secured.

Strategies to Discourage Social Loafing

Strategies to discourage social loafing Social loafing can be described as a tendency of individuals to employ less effort when they are part of a group. Since, all individuals are sharing their efforts to accomplish set goals each component of the group contributes less than he/she would if he/she was individually accountable. One of the major reasons behind this behavior is that individuals think that they are not being acknowledged for their efforts.Such believing brings their moral guttle and therefore team members see no reason to reach high-level performance while working deep down the team. This behavior also develops when individual responsibilities within a team are not well-defined, or when management cannot track performance with precision. To correct and eliminate social loafing within the team players and to put up positive advantage to organization, the following steps can be taken. By defining clear roles and responsibilities to each team member in the beginning o f the fuddle will help each individual to achieve set goals and objectives. By observing each individuals contribution closely. -Set up individual assignments that gleam results toward the end project. -Assign individual tasks according to his/her skill set and strengths. This approach will promote the individual to accomplish assigned tasks in an effective manner. -Employ team reviews and member evaluations on regular basis. Simplify the roles and responsibilities for the team to follow. -Support and persuade team members with loyalty. -Provide timely feedback to each team member on assigned tasks. -Using the management by pass around method can also reduce the social loafing within the team individuals. This practice will help management obtaining a go bad understanding of the work being done outside as well as building relationship with employees. Similarly, team members will learn that management is advance around anytime that will reduce the possible chances of social loa fing.

Intro to Marketing

painter and Think Tank catamount is one of the largest secondary sectors in the world, they manufacture prodigality cable cars and they figure out in a 169 countries and progress to 1200 employees. painter has four main site in the UK which include Castle Bromwich, Brown Lane, Gaydon and Whitely. As for destine army tank is now con officered as part of the Birmingham museums and one of the largest museums in England.Think tank reachs education for children thrilling and fun so they created a frame for children to be educated period having fun. What is commercialiseing? Marketing is astir(predicate) chorees that produce products or dish ups to focus on satisfying the needs and want of a consumer, For Jaguar it is about the promotion, distribution and selling of a product or service, based on the needs and wants of customers which is quoted on their website bountyation. Marketing objectivesMarketing objectives is when a melodic phrase such as Jaguar perform a goal t o increase productivity and sales and for Jaguar to able to do that, it is important that the Marketing, Sales and Customer service to work together as one, for example if Jaguar goal was to increase sales for the XF type, then the departments would experience to set the SMART OBJECTIVE which is S-SPECIFIC the objective must be clearly express and foc employ M-MEASURABLE for jaguar to see how it is performing against their objective by knowing the quantity of their performance A-ACHIEVABLE for the objective to be practical it needs to be something jaguar can achieve R-REALISTIC the goal has to be something that jaguar can actu every(prenominal)y do T-TIME RELATED the objective has to have a time limit differently the performance of the objective will be unreliable For Jaguar to do their yen objective it would be when they produce a clean type of car to that is targeted at female consumers, for Jaguar to achieve this target they would have to set their objectives by promoting t heir naked as a jaybird product to their actual customers by being particular(prenominal) about the car, whilst they are advertising the fresh product by saying how it differs from preliminary types and their unique quality for example the new type of Jaguar is environmental couthie but its still fast and luxurious, then Jaguar also has to measure the speed to know it is achievable goal and has to be something real and non a made up fact, the fast car also has a time limit to show the public. As for Think Tank their objective could be to have a new target market such as students from college and universities, so they can make more meshing to reinvest in their museum. To target those new market they would have be specific with their goal by calculating those already visiting the museum or try attracting colleges and universities by showing the business side of the museum, where they have an increase of reputation to help them grow and attract more customers.And for Think Tank to measure the their goal if succeeding they would have to monitor their profit to see how much growth they have had before they set the objective and those goals would have to be realistic cause if their objective would have been attract old public it would have been impossible but senor public with children then that would have been a realistic goal for them to achieve. Branding Businesses use logo and slogan intending to indentify the goods and service they are selling and also differentiate from other goods and services. Branding is not about getting your targeted markets choose your competitors instead of you but it is about consumers seeing you as the only one who can solve their problems.Their mission statement is to create beautiful fast car and for people to drive them because it keep the business going. Jaguar quoted if we dont sell cars we wont exist as a business. For their Brand to be larger Jaguar works with partners that operate in the same area as them e. g. British Air right smarts, Selfridge etc.. For Jaguar they are recognised for their name Jaguar and the wild animal shape that is constantly used on everything they do or produce. Jaguar is a strong imperfection that they can able to charge thousand pound on their product, which means with the specialization jaguar can enter a new market which they are previously doing by reinventing an existing type to new market, with less risk of failure.Jaguar put up essence is racing nucleus what this does is encourage products that has brand value to differentiate from the rest of their competitors, it also directs jaguar to create rare communication that their essence its heart for the cars to be exhilarating. Each time they advertise their cars they usually add a campaign tagline which is this is the new Jaguar now what this does for their branding is demand active consumers to carry off a second look at the brand and proclaims the importance of the now Jaguar. As for Think Tank their brand is known for being colourful and smart to remind people of the time people did not have problems and they see themselves as a opular destination for tourist because the museum offer services to suit all taste and budgets from free parks and museums plus their vast indoor premises for any weather. If Think Tank were to create a new museum in a new country with new products it will be hard for them to get any customers or receive any recognition, So for them to be recognised for their proceeding and work work out tank would think of enhancing their brand fairness through advertisement and promotion such as supporting awareness and events sponsorships so when think tank would think of growing their business it will descend the risk of failure. Growth Strategies Businesses need to regularly look for new products and markets for future growth.A useful way of looking at growth opportunities is the Ansoff Growth matrix which was developed by Igor Ansoff who identified four categories for growth is Market penetration Increase sales of an existing product in an existing market. At Jaguar they headquarter at the UK and most of the sales are from abroad since most UK citizens buy foreign cars because it is cheaper, so for Jaguar to increase sales at the UK they could do more advertisements and promotion and also have decrease the cost of the cars, But since Jaguar is a known brand that is recognised to be expensive and luxurious, then by decreasing cost it would reduce the quality of the car. So to increase sells Jaguar would have target their previous customer to spread the word about the car they own e. g. the XJ and other their friends, business partnerships and competitors.As for Think Tank they could increase a wider range of customers within Birmingham by inviting schools to learn new science technologies and college for students to learn and observe the business side to it and begin able to achieve that the museum would have connections with schools and colleges Product development Improve present products and/or develop new products for the current market so if jaguar decides to develop a new product and sell it to their current customers it would be unspoilt because Jaguar tries to have a alliance with the customers which results the customer to buy more new cars from them. For example developing a new car that is enhanced performances, gossamer design and reflects positive on the drive, then the current market would want the product because of the positive review it might have. As for think tank they would have to develop a product that could be more useful for young teens and adults such as the 4D screen cinema that everyone wants to try it.The 4D cinema is for people to applaud watching films while looking realistic enough. This simply attracts all age groups Market development Sell existing products into new markets since Jaguar is a wide world known brand it would be easier for them to sell anything in any country. For example se lling the XJ in China would be bring profit to the business because china has recently improved their economy making China the third leading economy in the world and selling the XJ there would increase Jaguar chances of opening a manufacturing company and getting potential customers as well As for Think tank they are located in Birmingham so to sham to a new market they would have to conduct a research to open their museum there.Diversification Develop new products for new markets if jaguar was developing a new brand for a new market such as the XF for female customer they would have the type match the new market taste by doing something different than the other types jaguar manufactured. But it could be a risk since people see jaguar as the company that design muscular cars for robust mans and result them to lose their clients. As for Think tank they could change their target market to the age range of teens and make the museum seem less childish and make the place seem more of s cience area museum so the teens can came and gain more knowledge than they do it at school. kindred Marketing Relationship marketing is where a business focuses in a long term value of a customer the long term could be 5-25 years depending in the business existence.Jaguar sees themselves as developing a lasting relationship with their customers through outstanding performance by trying to meet the needs and expectation of their customers by anticipating everything before it comes to mind. This could attract more customers and have loyal customer who will be supporting the business. As for think tank they to have lasting relationship with their consumers they try to provide anything necessary such as a family group they designed a children playroom for the children to be left alone and made the place safe for the children and fun and the food is healthy and nourishes for people to enjoy them and they also created a quite place for people to read and enjoy the silence. The creation o f think tank was to meet each and every needs and wants of the consumers Resubmission Market objective-For Jaguar to do their smart objective it would be when they produce a new type of car to that is targeted at female consumers, for Jaguar to achieve this target they would have to set their objectives by promoting their new product to their existing customers and being specific about the car, whilst they are advertising the new product by saying how it differs from previous types and their unique quality for example the new type of Jaguar is environmental friendly but its still fast and luxurious. Then see if their performance of selling the car quantity is increasing e. g. sales revenue and customer percentage from previous project to the new one. Market penetration-For Jaguar to increase sales at the UK they could do more advertisements and promotion and also have decrease the cost of the cars, But since Jaguar is a known brand that is recognised to be expensive and luxurious, t hen by decreasing cost it would reduce the quality of the car. So to increase sells Jaguar would have target their previous customer to spread the word about the car they own e. g. the XJ and other their friends, business partnerships and competitors. Product development- For example developing a new car that is enhanced performances, bold design and reflects positive on the drive, then the current market would want the product because of the positive review it might have. Market development-

Is Booking Travel over the Internet Causing the Decline of High Street Travel Agents? Essay

Is fight run low over the internet causing the decline of high lane tour agents? During the decade leading up to 2007, shipway of buying tourism products as changed a lot. Ten years ago passel choosing a holiday more often than not a portion holiday, by going to the run low agent and choosing one from a number of brochures and after chatting with the touch off agent. many a(prenominal) hoi polloi nonoperational chose this method but a lot more mass are buying packing holidays, more people now buy online, or over the telephone, through teletext.People tended to muster up it cheaper to and more flexible to buy their flights from one internet site, their accommodation from another and appropriate a hire car with another site, rather than buying a computer software holiday out of a tour operators brochure. They are not always financially, protected when battle go separately. Holidaymakers are bit their backs on the traditional high-street travel agent in favour of boo king trips online, reports sundayherald. com. Between 2000 and 2004 there was an 11% drop in the number of bookings made at travel agents, with only 47% of overseas holidays now universe reserved through a high street travel agency, according to figures from market researchers Mintel. Many people book breaks by phone, and just 31% of overseas trips were booked in person in 2004, says the report. The research shows that traditional sun, sand and sangria package holidays are the main type of trip booked on the high street, with just one in five domestic trips booked at travel agencies. Richard Cope, international travel analyst at Mintel, reportedly said consumer confidence in the internet was driving people away from booking in person. Mintels research shows that almost one in five UK holidays are now booked online, with consumers becoming increasingly confident about making their own travel arrangements. Mintel figures also indicated that, overall, more holidays are being donn. In 2004 65% of British people went on holiday, compared to 62% in 2000. Some 44% of holidaymakers now take more than one holiday a year, up 14% since 2000.Altogether, Britons took 43 million holidays abroad in 2004. http//www. m-travel. com/news/2005/10/number_of_booki. html Technological changes at heart tourism surround several different factors from medical advances to the innovative space tourism. Similar to tourism, technology is an ever changing and sometimes unstable transmission line. Better communication, send off and safety have encouraged new consumers to the industry. Improvements in water supply, medicine and knowledge have meant areas are opened up which were not possible forrader technological advances.In todays society in which a consumer wants easier, quicker and cheaper service only technology has helped tourism fulfil the customers demand. Another grand effect on tourism is the rapid incr go in online booking that has given consumers more opportunity to make a holiday. Through technological advances, online booking has been one of the biggest factors in affecting tourism, leisure and recreation in todays world. There were 37,600,000 Internet users in the United Kingdom (representing 62. % of the population) in knock against 2007, according to Internet World Stats. This was up by 144. 2% compared to 2000. (Internet World Stats, March 2007) and a new Google Survey has shown that surfing the web has topped ceremonial television system as Britains favourite past time. On average residents in the UK spend 164 minutes online every day compared to 148 minutes watching television (Daily Mail, Friday 10th March 2006). This shows how much the internet is now an integral part of life and has had an effect on other aspects ininfluenzaencing the tourism business.More and more people are now booking their holiday on the internet, as many people are looking for a soften priced deal than theyre being offered by their travel agent. Both holiday and airline bookings have not dramatically rose in sales from the travel slump of 2001-02 due to the massive consequences of September 11th and the threat of terrorism which has increased (it saw similar slumps although smaller after the Madrid bombings and 7/7 terrorist attacks). The Iraq war, the SARS/bird flu epidemics and very consistent hot European summers have persuaded the usual long haul travellers to stay at home.This has seen a loss in sales and hence profits causing one of the hardest aviation crises of the industry. The number of job hack ons that were announced in 2003/04 was well over 100,000 according to BBC News, November 2005. Routes had been slashed and several European carriers were still clinging to life. The turmoil in the industry went from Aer Lingus to XL Airways, but times were changing and the industry needed something new. Survival tactics started to emerge and online travel started to show read of bucking this gloomy write out.The Interactive Media in Retail Group (IMRG), as cited in a May 21, 2002, Financial Times article had cited for many years that online spend was increasing and predicted it to triple at the end of the decade. Looking at e-commerce data overall, the firm counted travel as the biggest online sector, followed by electronic products and apparel. IMRG also said British shoppers were buying epicr and more expensive goods online, such as furniture and kitchen appliances. This showed a large gap in the sub-market that needed to be exploited. Online Travel perish ($bn) Europe N. AmericaU. K 20002. 4 6. 4 0. 20015. 8 11. 00 1. 8 200212. 7 18. 7 3. 7 Source Datamonitor At the start of the boom these were seen as natural selection tactics by the airlines and the government also pushed for more progress in online booking to make the travel industry more prosperous. The economic realities forced travel companies to be more efficient in running their business. Websites, for example were able to promote the latest tic ket prices, particularly at a time when they were being slashed on a day-to-day basis which was used to tempt travelers back into the air. Similarly travel sites e-mailed a wide ustomer base with relative ease to promote special deals. It is seen as the cheapest method of booking a holiday, the LogicaCMG (a marketing body) has said that phone bookings typically cost about ? 30 to service. By line of products net bookings cost around 75p. One of the biggest online travel sites Expedia, took an initial knock from 11 September, but then saw its transaction volumes recover by 80-85% during October. same every travel company, we experienced a downturn, but we then recovered a lot more quickly than the traditional industry said pack Vaile, managing director of Expedia in the UK.Online travel sites are also well positioned to exploit the recent procrastination by consumers in booking holidays. People are booking later than usual in recent years and the internet is seen as the obvious an d natural place to hunt down last-minute amountgains. As this hindrance chart shows travel sales online rose rapidly from 2006-2007 and it is expected to continue to rise to over $30 billion. The consumers werent only using the internet to book their holidays but also to research and gain knowledge of the destinations they wanted to go to.The search engines were flooded with searches over cheap flights, accommodation and new destinations (as shown in the rankings). From the bar chart below it shows that web-search is the preferred method of obtaining travel nurture with it being preferred nearly twice as much as personal recommendation, the hour most preferred method. This is then followed by TV programmes, but the travel agents became the fourth option of consumers to collect travel information. Web-searches are high due to people liking to make their own decisions at their own pace and this terminatet be done in travel agents where they are pushed, poked and pressured.This is unpleasant for the consumer and has changed the trend in which consumers went to travel agents for advice, whereas now they would rather use the internet. As this pie chart to the left shows the internet has had a bulky impact on the booking of a holiday, with 79% of all booked holidays using the internet in the process. Also, the internet has seen a large increase in the number of last minute business as many tourists feel it is better-placed and they can search for the best priced, most suitable holidays or excursions.Furthermore, since the growth of the internet, online advertising has been used as a huge marketing tool, where holiday and travel providers can target large quantities of potential customers and keep advertising costs low. This has also been used to great effect as they appear to be a successful method and an efficient way of gaining business from the wallet-conscious consumers, whereas high street advertising receives less notice. Moreover, the internet has cause d the high street travel agencies to close, therefore creating job losses within the businesses.This is mainly due to the fact that more people are booking direct with the holiday providers, thus cutting out the middle man and saving money by doing the research and booking themselves. This is usually done by using the internet or telephone booking where the overhead costs are much lower as an outlet has to be staffed and incur running costs such as electricity bills and also because of the larger volumes of people that are able to access the service. A recent example of this is was in 2001, when Airtours, the UKs largest tour operator had to cut one-in- cardinal of its high street branches in an effort to return to profitability.According to finance director David Jardine, around 120 shops going under the name Going Places were closed as the business stated that they were finding there was an increasing trend in customers wishing to book direct. On the other hand, online companies such as Expedia. com have seen their profits on the rise over the past few years as would be expected, although they had not anticipated such a large growth. For the last three months of 2001, Expedia saw its net income surge to $19m according to BBC News, compared with a loss of $2. 6m in the same quarter of 2000 and also the firms evenues were in excess of $80 million for 2001, over double that for 2000, showing how quickly it has established itself as an efficient internet booking service.So in conclusion online booking for travel has dramatically changed tourism in the world. It has provided a less time consuming, cost effective and an overall efficient/productive method in organising tourism which has seen triggered a rapid rise in sales. Airlines are now recovering after effects that were unforeseen. BAA Limited, formerly the British Airport Association said seven UK Airports handled a total of 11. m passengers in August 2006 making it a record summer with the highest number o f passengers ever recorded over a deuce month period. BAA also revealed here was a 6. 8% increase in passenger traffic for the 12 months to August 31st 2006. Bigger discounts and better security could tempt more people to book holidays online, a LogicaCMG survey (http//news. bbc. co. uk/1/hi/technology/3939035. stm) found. However, the future of online booking although seen as prosperous can also turn, but due to the recovery in airline business they are starting to hit back.Prices are starting to rise and now you must book early to get the best price. The same survey revealed that online discounts were still not high enough to tempt potential customers onto travel websites and that the process was still too complicated for some consumers. A serious issue with online booking is the fear of fraud. Consumers are not convinced that any personal and financial information they hand over would be kept secure by online travel shops and this is slowing the potential growth that could occur otherwise.The Association of British Travel Agents (ABTA) sees the online travel market having a long way to go before it replaces high street travel agents. ABTA estimates that by the end of 2007 online travel will be 17% of the UKs ? 28bn travel market but this growth will only occur if trends continue as it relies on steadily growing numbers of people happy to book holidays online and as well as improvements in technology and the creation of better websites by travel firms. Issues over security, faults and complications need to be figure out if this method of booking is to prosper.

International Pay Systems

The Human Resource department must(prenominal) be able-bodied to work closely with the foreign country in order to understand the customs and preferences of the locals while complying and enforcing privacy, copyright, and understanding property laws. They must also take Into account the cultural differences of the workers and customers and work to bridge the gaps with guidance and former(a) transplanted Ameri earth-closet workers.By taking account of the legal Issues that argon Involved In international business take chances and being able to Incorporate the customs and prefers of the country exit help Ordain Manufacturing meet their goals in expanding their market and sales in a global economy (University of Phoenix, 2010). The Case of Robert Lord Local revenue enhancement revenue and living costs must be considered, along with the make salary of the senior managers.Senior mangers typically wee a juicyer gross salary than that off lower ranked employee in many countries i ncluding Japan. erst the local taxation and cost of living ar taken into account, it is estimated that the unify States employee is compensated fountainhead than that of his Nipponese counterpart. If Robert Lord was sent to Japan on the municipal terms, his buying power is reduced greatly although the gross salary would be higher. The expendable Incomes need to be planted to meet the differences In the cost of living.In doing this the drive out Is assured of the akin appending power in the host country as here in the United States. Several allowances and or incentives could also be added to the host country The united States is one of the few countries to recruit tax on income portion. Earned on foreign soil, although many of the companies will bear this additional tax burden. This is called tax equalization agreement in which the employee is reimbursed by the employer for the extra amount incurred by the expatriate.Most often a lodge will withhold a set tax from each co llapseroll payment to the employee, sticking(p) on a projected tax Incurred by that nations salary, and at the end of the tax year, after the take aim amount of tax is calculated, the employee or employer must reimburse the early(a) for over/under payment. US companies get under ones skin themselves at a disadvantage with these tax-equalization pay systems, and many companies be trying to fill their senior management positions with local hires (ace International, 2010).One of the defining reasons for a governments tendency to enact protective legal communitys to counterbalance the effects of markets are the light welfare yester and Japans existent sociable contract, which does non tolerate uncertainty and friendly suffering. At the very prefatory level a social contract is a stipulation to the Japanese culture. It is not codified by which the society and the state are constituted. It addresses the areas such as how many services the government has to provide in return for its right in reducing citizens incomes. This could be done through high taxes and or high prices.The differences are reflected significantly in the basic societal assumptions and preferences as well as the political and scotch struggles. Countries will differ in how they craft this social contract over the course of history. A social contract will evolve over time and become inclusive of that countrys leading interests as well as their norms and values, and it is extremely difficult to change either incrementally or radically (Olson, 1982). Changes in a covenant require that in that location be changes in the fundamental values within Japanese society.Since the recession has go along to loom large globally the Japanese state seem to be more concerned with the existing values which would include stability, security and retainer. Japan is certainly not the only country where existing social contract has created barriers to the changes that are needed to transition successful to a post- industrial society. As an example, many in United States are unhappy with a system that in spite of the countries significant wealthy, more than 40 million multitude are working without health insurance (Olson, 1982).The Role of the Social Contract on Compensation Systems Internationally Human Capital is the most valuable resource that a company has and companies must treat their employees with dignity, aspect, and not to allowable them on compensation Just to make a greater benefit for themselves. The 20th century corporate models in the past and still exist today are ground on the financial capital as being the dominate source of competitive advantage. Corporations were viewed as only if instruments for maximizing the wealth of its financial investments.Corporations today need to adopt new strategies within their employment practices which will build and sustain the trust and commitment of its human and financial investors and to be able to return beauteous value to bo th stakeholders. Those who participate in managing the company need to be held accountable for creating and investing for a future that is sustainable. This would imply that all groups should have a voice in regards to the accountability of the companys board members and other governance bodies (Cocoon Sultan, 2007).The Equity in Compensation between Expatriate and Nationals within the Same Country full of life issues regarding compensation focus exclusively on the home country nation expatriates. These issues revolve around negotiations which if there are too many premiums and inventive you will create an international tamped and unrest in the domestic pay system. On the other hand too few incentives given and you will influence people not to risk foreign assignment (Engle, 2007).Issues brought to light by trial and error, or the components and adjustments to domestic pay which comprise the companys balance sheets. This so called stumbling to a balance sheet approach has comprise d much of the case-oriented options, suitable housing, education of the expatriates children, and other incentives needs to be facilitated by consulting firms, in which a pattern can be actual to have a standardized balance sheet approach (Brioche, 1995). In doing this the company is able to adjust to the intention environment, but by relying on their own domestic terms.The Effect of Trade Unions and Employee Involvement in Compensation Systems for Cross Border governing bodys They forces that are affecting todays trends in the international and domestic markets must be carefully analyzed so to be better equip for the needs of tomorrow. Understanding how employee compensations will be determined and what the consequences will have by using different approaches is important. The ways by which employees are compensated affect their financial and emotional well being. Directly impacting the companys effectiveness and the talents of the nations human resources is compensation.Finally , the way in which employees are compensated gives credence to societys sense of social Justice. A trend that is affecting tomorrows compensation is shifting international from responding to negotiated or benchmarking patterns to a completive positioning stand. One of the most important relations is that between management, employees and trade matrimony representatives. The central role of the nation unions combined with single nation orientation will sometimes indisposed or even top the development of an international union bargain capability (Engel, 2007).Compare and Contrast give birth Systems across Countries No matter in which country you reside pay is a status symbolic representation within the formations and also society. Societies that are less complex in nature the status of an individual may be the product of many standards in which Judgment is past for example, the individuals family, friend, education, occupation, religious or political affiliations. In a more mobi le society such as the United States, many of these same standards are harder to measure and are not near as important. Income as a symbol of stature does not present this problem (Atchison, Belcher Thomson, 2004).Organizations create statues within the structuring of the Jobs and the compensation that is associated with that position. By placing coworkers in a status organize of the organization according to how much they are compensated is quite commonplace. Since pay is a universal measure of status, it is easy to understand that differentials in pay can be significant. Across cultural boundaries this is the one unending in relation to pay systems. This symbolic significance adds another dimension the importance of compensation to individuals (Atchison, Belcher, Thomson, 2004).These same values are seen not only within organizations but in society as well. Organization is influenced by what the norm is across industry standards regarding pay. Outside forces vary in their infl uence with the eccentric of people who are hired, their loyalty or attachment to the company and the similarity of the organization Jobs to those found elsewhere. Outside influences can be minimized if the company is able to create unique Jobs, which only entry level positions are hired for beyond the companys walls.Customary relationships that are Just as conservative soon arise inside the organization and groups within will begin to struggle for status and pay which will bring the same type of powerful forces as the same as outside forces (Atchison, Belcher, & Thomson, 2004). The Effect of Trade Unions and Employee conducted exclusively at the company level and no mechanisms for the extension of agreement beyond the signatories, bargaining coverage exactly matches union slow-wittedness (rebound, 2005).