Outline Essay Example | Topics and Well Written Essays - 250 words - 9

Outline - Essay Example The introduction part points out that some of the basic economic factors like price, income and unemployment can largely affect the whole social and political systems within a country. The body part of the paper analyses aggregate demand curve and aggregate supply curve, with emphasis on how price and other determinants like technology, consumer spending and wealth, exchange rate, investment and government spending etc affect aggregate demand and aggregate supply curve. With the help of diagram, the paper emphasizes that the curve of aggregate demand will always be downward sloping because of major three factors that are real-balance effect, interest rate effect and foreign purchase effects (McConnel and Brue, 194- 195). With the help of both short run and long run aggregate supply curves, the paper addressed key factors that can shift the curves. The paper concludes with opinions from author’s point of view and with suggestions for conducting further analysis on the effects of exchange rates and impacts of unused resources on long run aggregate supply

Factors Affecting Capital Budgeting of ICT Sector Firms

Factors Affecting Capital Budgeting of ICT Sector Firms This research proposal has been written to compare the factors affecting capital budgeting of firms in Information and Communication Technology sector in Thai. The survey factors of decision making in capital budgeting. The many decisions that top management must make in firms. This method is one duty of a financial manager to choose investments with satisfactory cash flows and rates of return. The factors affecting to make decision in capital budgeting, which is the allocation of funds among alternative investment opportunities, is crucial to corporate success. The explicitly considers how well-managed companies and the competition to hook up in segment market of in information and communication technology sector. Overview of Information and Communications firms in Thailand The most economies in the world people consume by spending money to buy goods and services. The ultimate aim of business is to maximize the market value of the firms common stock. Whereby, this means the wealth of its shareholders (Sharpiro 2005). The purpose focus on shareholder value begins with the simple economic understanding. Therefore, the roles of current business can growth through affecting quality competition. This research proposal has the interest in the sense of decision making style in ICT sector. Competitions exist to give the opportunity to enter the best competitions to be found in this kind of business in Thailand. A more captivating reason for focusing on creating shareholder wealth is the difference between the values of the company. Moreover, Companies in ICT sector are highly competitive market in Thailand. That the reason why the significant decision making of capital budgeting to invest by critical thinking. Verma et al (2009) observed for achieve the firms a re focusing even more on effective financial management practices and are greatly concerned about core financial issues like capital structure, cost of capital, working capital management and capital budgeting. The objective of capital budgeting In the recent years, managers have become more sophisticated in allocating capital resources and more concerned about return on investment. Sharpiro (2005) shows the important discussion is that the primary objective of financial management is to maximize the shareholder wealth. In other to, we need to know what affects wealth to benefit shareholders. Consequently, one way that people acquire more wealth is to defer invest and consumption in a company. Those who are relatively risk averse become bondholder, lending money to the company and repayment of the loan.In reality, any firm has limited capital resources that should be allocated among the best investment alternatives. The argument that capital is a limited resource is true of any form of capital. Management should carefully decide whether a particular project is economically acceptable. In the case of more than one project management must identify the projects that will contribute most to profits and to the value or wealth of the firm which is the basis of capital budgeting. Stout (2008) expresses the process of evaluating the desirability of investment is referred to a capital budgeting with real options. Furthermore, illustrate how to price a capital investment project containing real options. To explain these concepts to a wide audience in accounting In addition, this research proposal represent evaluate business strategies on the basis of prospective in capital budgeting by opinion managers who controls the capital resources is managerial decision from sample companies in ICT sector, which is good for every one, not just shareholders. It is well for politicians and other commentators to reflect on the facts in issue. Critical review of Literature This research generalized how company make financial decisions that started by explaining what these decisions are and what they are seeking an achievement. The secret of success in financial management of a corporation depends on how well in system of corporate governance to increase value. In other wards, maximizing value is like advising an investor in the stock market. To carry on business, a corporation needs a limitation to describe investment decision. The investment decision also involves purchase of assets that are often referred to as capital budgeting. The most corporations focus on capital budgeting listing the major project approved for investment. Investment proposal come into view from many different parts of the organisation that may have concluded the simple choice of which projects to accept or reject. Hence corporations need processes to ensure that every project is assessed consistently. The future investment outlays in most companies depend on the investment proc edure starts with the preparation of annual capital budgeting that is a list of projects planned for investment decision. The investment decisions let project proposals from companies for review by planning staff who controls the disposition of corporate resources is making financial decision (Brealey et al., 2011). Furthermore, Burns and Walker (2009) represented the capital budgeting process has been described in terms of four stages: Firstly, Identification is idea generation that include how project proposals are initiated. This stage composes of the overall procedure of project including sources origination and reasons for idea creation. Besides, process of origination and submission procedures are interested in an incentive system for rewarding good ideas. Moreover, this stage focuses on time pattern of creation and what level projects are generated that is a formal process for accepting ideas. Stanley and Block (1984) surveyed there has never been an in-depth survey in this stage. The responding companies in capital budgeting proposals originated bottom up over 80 percent versus top down. Secondly, Development also focuses on the details of how the data is estimated that which firms use cash flow versus accounting data. This involves the level of review, the role of project size, organizational structure and the initial screening process which rely upon primarily early screening criteria and cash flow estimation. Pruitt and Gitman (1987) identified the origination of biases in process for a deeper understanding of capital budgeting forecast and cash flow estimation. In addition, they considered financial, marketing, production and economic factors for quantitative forecast. Gordon and Pinches (1984) suggested the role in forecast accuracy and emphasis on the importance of information systems processes that were the key to improvement of capital budgeting. Thirdly, Selection includes personnel involved and the techniques used for the detailed project analysis that results in acceptance or rejection of the experimental project for funding. This stage separate to subsections follows as: 1. Personnel study on determining person who controls the disposition of corporate resources in company is making final decision and analyses capital expenditures. However, this includes amount of people are involved in project. Brealey et al. (2011) suggested the problem of biased forecasts that originated from strategic planners may have a mistaken view of forecast because cannot identify all worthwhile projects. For instance, the managers of project A and B cannot be expected to see the potential economies of closing their projects and merging production at new project C. 2. Reason for selection Techniques includes determining some techniques are preferred. According to Verma et al. (2009) demonstrated Companies invest in long term assets that expected a flow of benefit over the lifetime of the capital asset in project and a certain amount of resources in exchange for the future return that involves risk. Moreover the many capital budgeting methods or techniques are available for these investments or projects evaluation. A comparative study of affecting capital budgeting by evaluate the impact of different factors or variables on the selection of an individual capital budgeting. In addition, this research covers capital budgeting principles and techniques. Shapiro (2005) represented the companies can use to evaluate prospective investments. To accomplish this object by translate the basic principles of capital budgeting into evaluation techniques capable of applying these principles. The several different methods evaluate potential projects that manag ers use to analyse investments. The alternative methods include: Firstly, three discounted cash flow techniques net present value, profitability index and internal rate of return. The techniques are defined as follows: Net present value (NPV) is the present value of the projects future cash flows that discount at appropriate cost of capital and minus the initial net cash outlay in cost of the project. The value placed on a prospective investment project that focus on cash and only cash, account for the time value of money and account for risk. Thus, projects have a positive NPV that should be accepted. On the other hands, a negative NPV should be rejected. Moreover, Comparison in many projects that the one with higher NPV should be accepted. This NPV method focuses on all cash flows and the time value money when takes into account. Profitability index (PI) is defined as a project equals the present value of future cash flows divided by the initial cash investment as known as the benefit cost ratio. The project should be accepted if the ratio exce eds 1.00. NPV and this ratio always yield the same accept-reject decision. Sometimes, PI can provide superior decision in investment. Internal rate return (IRR) is defined as the sets of present value in project of future cash flows equal to the initial investment outlay that is a discount rate. In other words, this ratio equates the project when NPV is zero that determines the maximum interest rate. The rationale in project yielding more than its cost of capital should have a positive NPV and should be accepted. Otherwise, the project should be rejected. Secondly, two non discounted cash flow techniques payback period and accounting rate of return. The techniques are defined as follows: Payback period is defined as length of time necessary to recover it takes before the accumulative cash flow equals the initial investment from net cash flows. The payback rule states that project should be accepted if payback period less than some specified cut-off period or less period than others project. Payback period was a most commonly to use when choosing among alternative projects. Although widely to use this method, it has serious weakness because this method ignores the cash flows beyond the period and the time value of money that is very sensitive in investment decision. Accounting rate of return also called as the average return on book value or the average rate of return. This technique is the ratio that defined average profit after taxation to average book investment this is an average return on investment (ROI). A return in investment yielding grater than in comparison project and standard should be accepted. Whereas the result is below should be rejected. In addition, Verma et al. (2009) represented the comparisons capital budgeting techniques used in practice. A non-discounted cash flow in capital budgeting techniques was increasing in 1960s especially the payback period method. On the other hands, a discounted cash flow in capital budgeting techniques were interesting 1970s especially use of internal rate of return method in. A trend towards incorporation focused on risk that was also indicated by many studied. Furthermore, the most preferred method for evaluation of investment risk that depended on sensitivity conservative and analysis forecasts and the payback period method and followed by internal rate of return method were most popular in 1980s. Authors found that evaluators used multiple evaluation methods that internal rate of return and followed by the net present Value method were the most preferred choice in 1990s. The adjustment of discount rate methods were the most widely accepted discount rate that was the weighted aver age cost of capital (WACC) that Authors found 78 percent. In the 2000s, Peat and Partington (2007) demonstrated the most popular project evaluation techniques were net present value, internal rate of return and payback period that the most of companies observed these techniques. 3. WACC is defined a usually estimated cost of capital that average rate of return demanded by investors include companies use this rate to make project selections. Bruner et al. (1998) represented the research that companies computed the cost of capital by using WACC. 4. Risk Analysis is actually defined in a capital budgeting context. The risk analysis methods focus on recognised, reflected and assessed. Shapiro (2005) represented the real options and project analysis, risk and incorporating risk in a capital budgeting analysis, corporate strategy and the capital budgeting decision. The improvements could be made in obtaining.The important input from management for improving existing risk models. Ken and Cherukuri (1991) represented the case of large U.S. companies that concluded sensitivity analysis was found popular for handling risk that measuring risk is 80 percent. Dhanker (1995) demonstrated companies incorporated risk by adjusting 45 percent used Capital Asset Pricing Model (CAPM). Shao and Shao (1996) found that firms were using risk-adjusted discount rates less often than risk-adjusted cash flows.In addition, Graham and Harveys (2002) surveyed large companies are preferred to use risk-adjusted discount rate while small companies more lik ely used Monte Carlo simulation for risk adjustment. 5. Capital Rationing include the decisions are made by the financial environment. The specific reasons in capital rationing indicate the correct project proposal biases. The reaction capital rationing is not simply to real problem in managers face that main reason was irresolution to issue external financing. Moreover, accepting projects are avoided highly risk averse by using capital rationing to make decision in company that correct for management optimistic forecast biases. In addition, Gitman and Vandenberg (2000) considered the maintain a target price to earning ratio or earning per share among 23 percent of the respondents using of capital rationing and 60 percent was a debt limit imposed by management. Thus, this improvement has been made on the characteristic of capital rationing. 6. Project Approval as defined the autonomy of divisional managers and the role of divisional manager in each of capital investment project and operating accept-reject decisions. Fourthly, Control involves how the evaluation of project performance. This stage considers by comparison the different in expected result and actual results that indicate the performance measurement. Gordon and Myers (1991) expressed the respondents had performed post-audits 76 percent. However, the post-auditing was not effective according to criteria that involved the use of risk adjusted discount rate cash flow methods, the documented policies and procedures. Unfortunately, the post-audit is unpopular decisions in a standard part of the capital budgeting process. Furthermore, Myers, Gordon and Hamer (1991) found companies by using discounted cash flow based audit procedures by using the data form the same study that result increased their performance in companies. In addition, Pruitt and Gitman (1987) reviewed an upward bias that management suspects that focus on the post-audit process. The optimistic forecasts were sometimes depended on psychological factors. The way to eliminate the psychological biases on future capital budgeting proposals that means the post-audit should provide objective information to remove psychological to effective capital budgeting. The important in control stage has resulted in the deeper understanding in both control purposes and continuous improvement for future decisions. The important contributions have been made in the omitted stages of the capital budgeting process. A set of well-defined capital investment opportunities suggested by several authors its impact on all four stages that the decision support system. Opportunities include focusing on a particular stage by using best practices perspective in the area of real options and project analysis to monitor the outcomes. Brealey et al. (2011) demonstrated the final capital budget must also reflect the strategic planning of corporation. Strategic planning attempts to identify business where the corporation has a competitive advantage that takes a top -down view of company. Research aims and objectives of research proposal The objectives of the study are to examine the capital budgeting practices being adopted by companies in Thailand. Specifically this study aims a comparative study of the factors affecting of different firms in capital budgeting in Information Communication Technology sector. The overall research focuses on objective as following: This objective examines the corporate practices regarding the techniques of capital budgeting used for evaluating an investment proposal. To analyse and compare the difference objectives of capital budgeting by using acquired data. This objective evaluates the impact of different variables or factors affecting capital budgeting on the selection of a method of capital budgeting technique. This objective analyses the corporate practices regarding risk techniques of capital budgeting used for adjusting risk in investment proposals. This objective includes the affecting factors in each project and corporate strategy that relate to the capital-budgeting decision. To evaluate processes and techniques of capital budgeting to improve decision-making and the quality of decisions. Research questions and / or hypotheses H0-What are the purposes and objectives of investment capital budgets in each firm? H0- The identification, development, selection and control stage does affect the making decision of capital budgeting to accept the project. H0-The level of capital budget project does affect the selection of investment. H0-What are a capital budgeting principles and techniques make strategic decisions preferred by companies? H0-What is the most popular capital budgeting technique affect to make decision? H0-Does the company use of multiple capital budgeting techniques? H0- what important factors of decision making are the consideration non financial factors for deciding capital budgeting investment by selected companies? H0- what are risk factors to use in Adjustments? Research Design Methodology for the research This section is essentially about justifying the terms of methodology. It addresses the particular appropriate data collection and analysis. By 150 the questionnaires have distributed go to still ICTs companies in Thailand. The Social Science Version 16 (SPSS software) was advantage from this questionnaire. Thus, imply incidence and percentage are the importance in the lead presents the conclusion spits the questionnaire, way statistics explanation is the importance of using analysis the data. Data Collection Data collected in standardised format from lot of observations based on specific variables and identify patterns between variables. Hence, Data will be collected via structured by questionnaire (see in appendix) a personnel in companies in information communication technology sector in Thailand. The population of interest is planning staffs that involve the project within different department in each company. According to the mention above objective a comprehensive primary survey is conducted of 30 planning staffs who controls the disposition of corporate resources is managerial decision involved of projects companies. The planned sample is 10 projects from different projects in company the amount of staff are surveyed depend on the level of project. Data Analysis Wrenn et al. (2007) represented the SPSS is used the way random simplify by applicability. This technique use to test general in the population that known information of being selected as part of the sample. This research has applied the explanation will of the statistics that Zikmund (2000) demonstrated the explanation and summarize about the people by average calculation that the mean and percent values are majority form in summary data. The acquired data will be analysed by using qualitative methods and data will be compared the actual factors in capital budgeting. The limitation of method used According to Saunders et al. (2007) demonstrated the way of questionnaire process depends on the technique of limitation use in the research that is taking time to collects the data. Moreover, they may take time in making completed profoundly might cause something delay in during procedure. The convenience of limitation is easy to filtration that personal researcher are appropriate more than the filtration from the people. Conclusion Nowadays, the Thailand business environment has become highly sensitive competition in Information Communication Technology sector. The capital budgeting decision necessary for a number of changes have taken place in the business and economic environment in domestic market. For achieving this, the keyword to success in financial management depends on only the professionally and competitive managed companies. The companies are focusing even more on effective financial management practices and company can thrive in such an unstable environment. In addition, the companies are greatly concerned about core financial issues. That the reasons why focus on the affecting factors for making decision in capital budgeting that companies should be improved financial management. Reference Brealey, R.A., Mayers, S.C. and Allen, F. (2011) Principles of Corporate Finance Global Edition. 10th Edition. New York: McGraw-Hill Irwin. Bruner, R.F., Eades, K.M., Harris, R.S. and Higgins, R.C. (1998) Best Practices in Estimating the Cost of Capital: Survey and Synthesis, Financial Practice and Educational. Vol. 8, No. 1, pp. 13-28. Burns, R.M. and Walker, J. (2009) Capital Budgeting Surveys: The Future is now. Journal of Applied Finance. No.1 2, pp. 78-90. Dhanker, Raj S. (1995) An Appraisal of Capital Budgeting Decision Mechanism in Indian Corporates, Management Review. (July-December), pp. 22-34. Garbutt, D. (1992) Making Budgets work. 1st edition, London: Chartered Institute of Management Accountant. Gordon, L.A. and Pinches, G.E. (1984) Improving Capital Budgeting: A Decision Support System Approach. 1st Edition. Massachusetts: Addison-Wesley Publishing Company. Graham, J.R. and Harvey, C. R. (2002) How Do CFOs Make Capital Budgeting and Capital Structure Decisions? The Journal of Applied Corporate Finance. Vol. 15, No. 1, pp. 8-23. Ken, L.R. and Cherukuri, U.R. (1991) Current Practices in Capital Budgeting: Cost of Capital and Risk Adjustment, ASCI Journal of Management, Vol. 21, No.1, pp. 26-44. Myers, M.D, Gordon, L.A. and Hamer, M.M. (1991) Post-Auditing Capital Assets and Firm Performance: An Empirical Investigation, Managerial and Decision Economics. Vol. 12, No. 4, pp. 317-327. Pruitt, S.W. and Gitman, L.J. (1987) Capital Budgeting Forecast Biases: Evidence from the Fortune 500, Financial Management. Vol. 16, No. 1, pp. 46-51. Saunders, M., Lewis, P. Thornhill, A. (2007), Research methods for business students. 4thedition, Essex: Pearson Education Limited. Shao, L.R and Shao, A.T. (1996) Risk Analysis and Capital Budgeting Techniques of US Multinational Enterprises, Managerial Finance. Vol. 22, No. 1, pp. 41-57. Shapiro, A.C. (2005) Capital Budgeting and Investment Analysis. 1st edition, New Jersey: Pearson Prentice Hall. Stanley, M.T. and S.B. Block (1984), A Survey of Multinational Capital Budgeting, The Financial Review. Vol. 19, No.1, pp. 36-54. Stout, D.E., Alice, Y. And Qi, H. (2008) Improving Capital Budgeting Decision with Real Options Management Accounting Quarterly Vol.9, No. 4, p. 1-10. Troung, G., Peat,M. and Partington, G. (2007) Cost of Capital Estimation and Capital Budgeting Practice in Australia Australian Journal of Management, Vol. 33, No. 1, pp. 95-122. Verma, S., Gupta, S. and Batra, R. (2009) A Survey of Capital Budgeting Practices in Corporate India The Journal of Business Perspective Vol. 13, No. 3, pp. 1-17. Zikmund, W.G. (2000), Business Research Methods 6th Edition, Fort Worth: The Dryden Press.

Human Cloning - The Greatest Danger is Ignorance :: Cloning Argumentative Persuasive Argument

Human Cloning – The Greatest Danger is Ignorance The successful cloning of an adult sheep—in which the sheep's DNA was inserted into an unfertilized sheep egg to produce a lamb with identical DNA—generated an outpouring of ethical concerns. These concerns are not about Dolly, the now famous sheep, nor even about the considerable impact cloning may have on the animal breeding industry, but rather about the possibility of cloning humans. For the most part, however, the ethical concerns being raised are exaggerated and misplaced, because they are based on erroneous views about what genes are and what they can do. The danger, therefore, lies not in the power of the technology, but in the misunderstanding of its significance. Producing a clone of a human being would not amount to creating a "carbon copy"— an automaton of the sort familiar from science fiction. It would be more like producing a delayed identical twin. And just as identical twins are two separate people—biologically, psychologically, morally and legally, though not genetically—so a clone is a separate person from his or her non-contemporaneous twin. To think otherwise is to embrace a belief in genetic determinism—the view that genes determine everything about us, and that environmental factors or the random events in human development are utterly insignificant. The overwhelming consensus among geneticists is that genetic determinism is false. As geneticists have come to understand the ways in which genes operate, they have also become aware of the myriad ways in which the environment affects their "expression." The genetic contribution to the simplest physical traits, such as height and hair color, is significantly mediated by environmental factors. And the genetic contribution to the traits we value most deeply, from intelligence to compassion, is conceded by even the most enthusiastic genetic researchers to be limited and indirect. Indeed, we need only appeal to our ordinary experience with identical twins—that they are different people despite their similarities— to appreciate that genetic determinism is false. Furthermore, because of the extra steps involved, cloning will probably always be riskier—that is, less likely to result in a live birth—than in vitro fertilization (IVF) and embryo transfer. (It took more than 275 attempts before the researchers were able to obtain a successful sheep clone. While cloning methods may improve, we should note that even standard IVF techniques typically have a success rate of less than 20 percent.) So why would anyone go to the trouble of cloning? There are, of course, a few reasons people might go to the trouble, and so it's worth pondering what they think they might accomplish, and what sort of ethical

Open Domain Event Extraction from Twitter

Open Domain Event Extraction from Twitter Alan Ritter University of Washington Computer Sci. & Eng. Seattle, WA [email  protected] washington. edu Mausam University of Washington Computer Sci. & Eng. Seattle, WA [email  protected] washington. edu Oren Etzioni University of Washington Computer Sci. & Eng. Seattle, WA [email  protected] washington. edu Sam Clark? Decide, Inc. Seattle, WA sclark. [email  protected] com ABSTRACT Tweets are the most up-to-date and inclusive stream of information and commentary on current events, but they are also fragmented and noisy, motivating the need for systems that can extract, aggregate and categorize important events.Previous work on extracting structured representations of events has focused largely on newswire text; Twitter’s unique characteristics present new challenges and opportunities for open-domain event extraction. This paper describes TwiCal— the ? rst open-domain event-extraction and categorization system for Twitt er. We demonstrate that accurately extracting an open-domain calendar of signi? cant events from Twitter is indeed feasible. In addition, we present a novel approach for discovering important event categories and classifying extracted events based on latent variable models.By leveraging large volumes of unlabeled data, our approach achieves a 14% increase in maximum F1 over a supervised baseline. A continuously updating demonstration of our system can be viewed at http://statuscalendar. com; Our NLP tools are 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 Table 1: Examples of events extracted by TwiCal. vents. Yet the number of tweets posted daily has recently exceeded two-hundred million, many of which are either redundant [57], or of limited interest, leading to information overload. 1 Clearly, we can bene? t from more structured representations of events that are synthesized from individual tweets. Previous work in event extraction [21, 1, 54, 18, 43, 11, 7] has focused largely on news articles, as historically this genre of text has been the best source of information on current events. Read also Twitter Case StudyIn the meantime, social networking sites such as Facebook and Twitter have become an important complementary source of such information. While status messages contain a wealth of useful information, they are very disorganized motivating the need for automatic extraction, aggregation and categorization. Although there has been much interest in tracking trends or memes in social media [26, 29], little work 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 challenging problem. On the other hand, individual tweets are short and self-contained and are therefore not composed of complex discourse structure as is the case for texts containing narratives. In this paper we demonstrate that open-domain event extraction from Twitter is indeed feasible, for example our highest-con? dence extracted f uture 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 only of interest to their immediate social network. 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 Descriptors I. 2. 7 [Natural Language Processing]: Language parsing and understanding; H. 2. [Database Management]: Database applications—data mining General Terms Algorithms, Experimentation 1. INTRODUCTION Social networking sites such as Facebook and Twitter present the most up-to-date information and buzz about 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 classr oom use is granted without fee provided that copies are not made or distributed for pro? t or commercial advantage and that copies bear this notice and the full citation on the ? rst page.To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior speci? c permission and/or a fee. KDD’12, August 12–16, 2012, Beijing, China. Copyright 2012 ACM 978-1-4503-1462-6 /12/08 †¦ $10. 00. is safe to assume it is of general importance. Individual tweets are also very terse, often lacking su? cient context to categorize them into topics of interest (e. g. Sports, Politics, ProductRelease etc†¦ ). Further because Twitter users can talk about whatever they choose, it is unclear in advance which set of event types are appropriate.Finally, tweets are written in an informal style causing NLP tools designed for edited texts to perform extremely poorly. Opportunities: The short and self-contained nature of tweets means they have very simple d iscourse and pragmatic structure, issues which still challenge state-of-the-art NLP systems. For example in newswire, complex reasoning about relations between events (e. g. before and after ) is often required to accurately relate events to temporal expressions [32, 8]. The volume of Tweets is also much larger than the volume of news articles, so redundancy of information can be exploited more easily.To address Twitter’s noisy style, we follow recent work on NLP in noisy text [46, 31, 19], annotating a corpus of Tweets with events, which is then used as training data for sequence-labeling models to identify event mentions in millions of messages. Because of the terse, sometimes mundane, but highly redundant nature of tweets, we were motivated 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 propose identifying im portant 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 appropriate 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 within a new domain. Supervised or semi-supervised approaches to event categorization would require ? st designing annotation guidelines (including selecting an appropriate set of types to annotate), 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 propose an approach to open-domain eve nt categorization based on latent variable models that uncovers an appropriate set of types which match the data.The automatically discovered types are subsequently inspected to ? lter out any which are incoherent and the rest are annotated with informative labels;2 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% Table 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 p hrase, calendar date, and event type (see Table 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 entities and event phrases are extracted, temporal expressions resolved, and the extracted events are categorized into types. Finally we measure the strength of association between each named entity and date based on the number of tweets they co-occur in, in order 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 u nique style. To address these issues, we utilize a named entity tagger and part of speech tagger trained on in-domain Twitter data presented in previous work [46]. We also develop an event tagger trained 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 to 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 Twitter’s 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 performance 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 roughly 30 minutes inspecting and annotating the automatically discovered event types. 2 In order to extract event mentions from Twitter’s 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 parts 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! CAN’T WAIT! These phrases provide important context, for example extracting the entity, 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 inference [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 precision and recall at segmenting event phrases are reported 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 manu ally annotated tweets (about 19K tokens). We compare against a system which doesn’t 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% estimated over as sample of 268 extractions) is su? ciently high to be useful for our purposes. TempEx’s 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 â€Å"tomorrow †), we leave adapting temporal extraction to Twitter as potential 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 manual e? ort is required to annotate tweets with event types.Third, the set of important categories (and entities) is likely 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 related 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 â€Å"next Friday†, â€Å"August 12th†, â€Å"tomorrow† or â€Å"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 Performance Fitness Interview ProductRelease Meeting Fashion Finance School AlbumRelease Religion 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 Celebration 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: Complete 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 induce 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 unsupervised information extraction [4, 55, 7]. Each event indicator phrase in our data, e, is modeled as a mixture of types. For example the event phrase â€Å"cheered† might appear as part of either a PoliticalEvent, or a SportsEvent. Each type corresponds to a distribution 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 calen dar dates in our model has the e? ct of encouraging (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 generative 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 phrase’s type distribution is shared across it’s 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 probabilistic queries about the data. This is useful for example when categorizing aggregate events. For inference we use collapsed Gibbs Sampling [20] where each hidden 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 given 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 season 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 – unveiled – 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 declared war – war shelling – opened ? re wounded senate – legislation – repeal – budget – election winners – lotto results enter – winner – contest bail 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 mone y 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 – glee – dvr – hbo net? ix – black swan – insidious – 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 – white 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 types 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 as sign 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 symmetric 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 categorization 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 events;5 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 users 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 along 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 arbitrarily 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 unsupervised approach to the supervised baseline, via a precision-recall curve obtained by varying the threshold on the probability of the most lik ely 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 user’s daily lives. As an example, users often mention what they are eating for lunch, therefore entities such as McDonalds occur relatively frequently in associat ion with references to most calendar days. Important events can be distinguished 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, and randomly sampled 50 events from the top ranked 100, 500, and 1,000. We annotated the events with 4 separate criter ia: 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 event’s 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 Fisher’s Exact test would produce more accurate p-values [34], given the amount of data with which we are working (sample size greater than 1011 ), it proves di? cult to compute Fisher’s Exact Test Statistic, which results in ? ating poin t over? ow even when using 64-bit operations. The G2 test works 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 comparable 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 don’t correspond to salient entities related to events; they often consist of single words which are di? ult to interpret, for example â€Å"Breaking† which is part of the movie â€Å"Twilight: Breaking Dawn† released on November 18. Although the word â€Å"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: â€Å"Twilight Breaking†, â€Å"Breaking Dawn†, and â€Å"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 estimate 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 â€Å"today†, â€Å"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 Other 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 Slide Park listening Music Release Hedley album Music Rele ase Wed Nov 9 EAS test Other The Feds 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 James 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 err ors, the majority of calendar entries are informative, for example: the Muslim holiday 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 (â€Å"entity + date + event + type†) are correct.We also compare against the precision of a simple ngram baseline which does not make use of our 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 even ts from news [43]. Recently there has been much interest in information extraction and event identi? cation within Twitter. Benson et al. 5] use distant supervision 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 narrow 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 Hash Functions [40]. Becker et al. [3], Popescu et al. [42, 41] and Lin et al. [28] investigate discovering clusters of rela ted words or tweets which correspond to events in progress. In contrast to previous work on Twitter event identi? cation, our approach is independent 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 Analysis We found 2 main causes for why entity/date pairs were uninformative for display on a calendar, which occur in roughly equal proportion: Segmentation Errors Some extracted â€Å"entities† or ngrams don’t correspond to named entities or are generally uninformative because they are mis-segmented. Examples include â€Å"RSVP†, â€Å"Breaking† and â€Å"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 oc cur on that day. Examples include locations such as â€Å"New York†, and frequently mentioned entities, such as â€Å"Twitter†. ing event-referring 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. Twitter’s 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 informat ion 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, this work extracts events, using tools adapted to Twitter’s 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. 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Impact of Media on Society’s Perception Essay

Introduction Mass media has advanced in terms of its expanse of reach in terms of technological innovations in the last few decades. With such expansion, representations of array of layers of portrayals and illustrations in all fields from the media have also risen (Klapper, 1950).   The movie industry in the United States had been one of the top exporters and income-generating areas of the country (Akre & Wilson, 2006). Its vast influence on the contemporary society conducts a huge impact on how the society is to internalize every movie by which they are able to watch.     Given the fact that movies vary in theme, in the message which it plans to convey, the 21st Century masses are believed to be in a state of threat on the increase of violence and other lawful circumstance which now places the world at the stake of conviction over what to watch, and what to make the children watch (Sharma & Dlouhy, 2004). Technological advancement The presence of mass media as a tool of communication has increased largely because of the technological innovations consistently being introduced not only in advancing the productivity rate of media organizations but also in expanding the capacity of the various media outlets to include a wider range of topics (Hudson, 1986). With this expansion, the subjects incorporated into the mass media has also been augmented (Graber, 1980) such that former topics that were once rarely untouched have now been constantly infused with unceasing publicity such as those that tackle Information and Communications Technology (ICT). Media as an information tool Contemporary trends in the media have not also failed to divulge into matters that concern individuals in various ways. Television documentaries as well as scholarly journals and entertainment publications have been constant players in bringing forth the issues that revolve around the lives of those who dwell and depend much on what they ought to see on the television. Apart from all these, advertisements have contributed largely to the depiction of the image of various women in a broad number of societies and fields of interest (Wortzel & Frisbie, 1974). Effect of media to women It is a fact that the media, in general, has procured a large number of specific portrayals of women that vary according to their age, national background, educational attainments and several other factors. Although there are articles claiming that the media has no role in the development of the women’s self-esteem (Wright, 1975), there is wide agreement among a number of researches showing that the media, indeed, brings both positive as well as negative effects on a woman’s self-image (Klapper, 1950).   By utilizing these studies that support the idea of the media harboring consequences to women, we can further proceed with the assessment on the effects of media by using the general argument that the media plays a contributive role in the creation of destructive self-images on the part of female individuals. Given the fact that women are the â€Å"most exposed† audience, the crucial points mentioned have been the center of several researches and studies that aim at shedding light and understanding on the consequences brought about by these media portrayals to women in general (Burd, 1939). Whereas a number of these researches and studies have shown that the media’s representation of women has been both a direct and an indirect factor in the development of a woman’s negative perception of the self (Greenwald, 1992), one can also attempt to take the opposite side by insisting that media’s representation of women has a negligible effect on the self-image of women based on gathered data and its interpretation (Burd, 1939).   While the underlying question being resolved by both sides of these scholarly attempts revolves around the question as to whether media has its effects on the self-esteem and perception of women, another fundamental question can be raised. Should the media’s representation of women be considered a factor in the development of negative self-image among women? That now raises the brow in the effects of media to the audience. Deliberative refutes with the observed impact on women With these things in mind, the main thesis that this paper will adopt is that the media’s representation of women has a negative impact on women’s perception on these representations from the media. Supporting evidences needed to sustain the claim are to be taken mainly from previous scholarly researches and academic studies that center on the self-esteem of women in the context of exposures with the images of women in mass media (Benas & Gibb, 2007). Other references needed to maintain the argument are also to be extracted from several theories that put a premium emphasis on the hierarchy of the needs of individuals and on how people respond to these needs (Olenick, 2000).   The lame fact surfacing on such thought dwells on the positivity or the negativity of the impact being obtained. The advent of electronic media   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Electronic Media’s advent and the consistent rise of age’s liberalism had endured in a profound argumentative impact with regard to social interaction as well as with cultural identity.   The changes made by mass media are evidently found on a series of notions due to consciousness, certain perceptions on reality and the palpable alterations of the masses’ individual lives concurrent on what had reconstituted by the mentioned technological change (Palmer & Young, 2003).   Technological or digital innovation dwelled on to by human beings had been observed to have been conducting a protective bubble of fixed racial, cultural and ethnic identity resulting to a sense of detachment which lies on the physical state of the screen persona as well as with the transcends in the reality of social culture (Barker & Petley, 2001). Effects of media on cultural views The intersections of the new media coherent with the transformation of relationships among individuals were seen to be among aesthetic traditions, context on contemporary matters and different forms of speculative futures (â€Å"The National Entertainment State,† 2006).   The effects of media on cultural views and perceptions of people have been constantly changing on to how the general media’s trend is now being implemented. An example of such would be best illustrated on many immigrants and workers in the United States who came from other 3rd World Countries.   According to survey, the impact of constant exposure on television or media mediums caused them to become more liberated and open (Sharma & Dlouhy, 2004) to some sort, given the fact that what they tend to often see on the television portray the reality of today’s generation for reasons which are all inter-related in a web of complex facts, figure and situations – tracing back on the history of television or entertainment, per se’. Media as a source of violence   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The level of morality of individuals are conceived to be one by which their respective behavior are dependently being implied to.   With regard to behavioral psychology, it had been stressed that an individual’s personality is the manifestation of the influences which one had been able to acquire all throughout his childhood carried until adulthood (Wright, 1975).    Americans’ somewhat insatiable appetite for violence had been depicted and described in the violence saturate of their culture.   In an article written by Lillian BeVier, she elaborated various examples on how media had taken a huge part on violence in today’s generation. Her findings led her to an echelon of realistic and obvious results such as: songs urging to rape women, killing police officers, committing suicide, and all other heinous crimes which are said to be the message conveyed by some songs (â€Å"The National Entertainment State,† 2006; Niemeyer, 1975).   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   However, the issue of violence being a triggering factor for such violence were not thoroughly given substantial evidence with regards to the exposure on media, she had clearly emphasized that there is a need for the government, the Congress, the Federal Trade Commission, nor any state legislature to provide the discretionary and lawful measures as having to be given the power to legislate such constraints to avoid producers or purveyors commit such insatiable mistakes on the field of media communication (Akre & Wilson, 2006; Barker & Petley, 2001). Laws as a mean in expunging violence caused by media Due to the rampant rise of violence which is anticipated to have been caused by media exposure, critics and analysts had made researches on how to expunge such problem within the country, also to help save the considered innocent victims.   Relevant questions had risen on varied situations. Aside from that, the numerous numbers of crimes which had been bugging the Justice Department had gone on massive state that the need of keeping the morality and the avoidance of such violence had been a must to be expunged.   The government’s aim in saving the viewing masses had been delicately found to be moving in a very slow motion that those who are concerned on the leech eating the morality and sense of dignity oh humankind are being alarmed, sending a series of requests to those who are capable of making laws, to focus on media regulation (Newton, 1996). Mimicking media symbols The impressions left by media and its impact on the society had been conclusively found that perhaps one of the major reasons for such was the extremist rhetorical views from talk shows viewed by the audience.   Sometimes, the openness and extreme liberalism of a person, being shared on television are serving as a guide or something which is then mimicked (Greenwald, 1992). In relation to such scenario, it shall then be one by which those who do not possess the higher level of rationality and understanding is most likely to follow what they believed were â€Å"right† and â€Å"factual† statements and examples, giving it a ticket on the world of crime.   It may not be a total form of proof for such acts, but it is clear that an ounce of influence may result on to the transformation of individuals, thus, only if it shall be given proper guidance that such â€Å"negative† transformation shall be avoided (Greenwald, 1992). Media crossing the line   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Furthermore, certain instructions may implicitly cause damage to families.   An illustration of such shall be seen on a scenario where families are to watch TV or a movie, with a theme on betrayal, prostitution or other forms of demoralizing schemes (Gunter, Harrison, & Wykes, 2003).   Given with such premise, although it will be under the discretion of the viewer on how to accept and internalize the message conveyed, still, it already gives the young minds, even those in the proper age an idea if such immorality. President Clinton once stated an impressive line on television giving the movie industry an alarm and a warning not to put across the level of immorality in the projects done for entertainment purposes.   He warned the participating subjects that â€Å"a line has been crossed – not just of taste, but of human dignity and decency†.   Such perception had then been crossing the stream every time sexual violence is given an amplifying catching tune.   It was then the time when the Hollywood’s dream of setting the â€Å"liberated† scheme of teen â€Å"adult scenes† were constrained and filtered (Barker & Petley, 2001; Newton, 1996).   Now that is pleasant news. Juvenile violence caused by mimicking media figures   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Juvenile justice system is aimed towards rehabilitating these young criminals, since they are not yet of the right age, and assumed to be not in the right frame of mind. But they could relinquish their hold on these criminals depending on the weight of the crime, or the court waives to do so (Burns, 1994). There is legislation for the protection, care, and custody of these children under their jurisdiction, since they are the ones that manage these legal concerns. People often deal with this delinquency problem by looking at the root of the problem: society and its components. People are driven into delinquency by various factors, including their outlook in the society and intervention by other people. In order to solve the problem of delinquency, these issues should be properly taken care off.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   People should start with their own homes and see whether they have proper communication with their children. Another is making sure that the environment is safe, free from other factors that could elicit delinquency. If it still couldn’t be managed, then that’s the time we need professional help. The authorities are more than willing to help, especially if the people are cooperating with them. There should be partnership with the authorities and the locals in order to solve the problem of juvenile delinquency. Music industry’s influence Moreover, there had also been instances with regard to the music industry.   Taking on to consideration the fact that music is now also a part of the television era, it is most likely inevitable for them to commit certain violations with regards to such.   It had been stated that the children of today are being exposed to certain music and videos which are offering negative images of human relationships as well as with unconstructive descriptions causing the downfall of the spiritual and moral values of the innocent individuals (â€Å"The National Entertainment State,† 2006).   Ã‚  Ã‚  An example of which are those kind of music which teaches men on how to mistreat women – and for women to â€Å"just† accept this kind of fate – all of which is nothing but a short of mental contamination. Perceivably, it has not only violated the view of respect on the side of women, but it already gives children a negative impression on how women are to be treated, and how they accept to be treated, in such manner (Groves, 2002).     Digging deep on such, for the reason that entertainment plays a big role in most individual’s daily activities, it is righteous enough that lyrics of songs are to be filtered so as to give consideration to those who preserve the essence of ethics and the decency in music, art, and all the further forms of entertainment enjoyed. Media impact on society’s perception   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Researches with regards to the viewer’s attitudes and behaviors toward television programs and portrayals as well as with the effects on the viewing experiences are found to be the major reason on how they are influenced and how they are able to internalize what they tend to see.   Viewers’ opinions about televised violence can vary significantly upon the physical form it takes as well as with the type of behavior displayed (Palmer & Young, 2003). Moreover, even the reasons for violence, the consequences for those who were involved, the nature of the perpetrator and the victim, and the relationship between them along with the setting in which the conflict and the scene has taken place, creates a huge function on how it shall be taken by such (Palmer & Young, 2003). Challenges in the rapidly changing media landscape   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Conceivably, the dizzying pace of change in the Tele-visual landscape providing stark realities, significant challenges, and opportunities are notable for children’s growth, education and socialization.   The transformation of such realities, challenges and opportunities shall be translated in their lives, bedrooms, and futures will largely depend on how revolutionary techs are implemented, funded, distributed and consumed (Groves, 2002). The unprecedented explosion in options of viewing, video and subscription video on demand, personal video recorders, interactive TV, interactive program guides, unfiltered Internet access and a set of new-handled/portable technologies which continuously emerges on the modern era are but creating a whole new environment on an ethical scale perception (Newton, 1996).   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The dull endless debates about media â€Å"effects† could be broken with relative ease only if journalists, policy makers, politicians and pundits could be shifted among their deference and devotion to the style which they now tend to swim on.   Instead, if such social psychological advent is to sieve on to persuasion and thus give hearing to some findings which shall serve a slap to their prudence then their perspective is most likely to be adhered justly (Groves, 2002). Conclusion   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The problem of violence in mass media which occurs in almost all corners of the world are but well-supported reasons and evidences why such problem must be given attention by the government.   It may not be clearly emphasized that it is the only factor why such immoral deeds are made, but then again, making it as â€Å"one† of the many factors is reason enough to alarm the society.     Such incidents raised and elaborated in accordance with the depictions on violent scenes and messages abounded on motion pictures, television, popular music, video games, books, and magazines featuring scenes of mayhem, sexual assault, murder, suicide, and all the other forms which are harmoniously repeated over and over again eat a large part on the apple of morality and behavior of humankind (Niemeyer, 1975).    In the same light, articles that touch on the opposite side of the claim are also to be utilized so as to review the probable refutations to the claim and to seek the best means in order to arrive at a solid conclusion on how mass media had been affecting the lives, specifically the perception of the contemporary masses so as to clearly understand the fallacious or the provided and proven facts and details with regard to how movies had been airing on television (â€Å"The National Entertainment State,† 2006). 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Children Who See Too Much: Lessons From the Child Witness to Violence Project. Boston: Boston Beacon Press. Gunter, B., Harrison, J., & Wykes, M. (2003). Violence On Television: Distribution, Form, Context, and Themes. Mahwah N.J.: Lawrence Erlbaum Associates, Inc. Hudson, H. (1986). New Communications Technologies: Policy Issues for the Developing World. International Political Science Review, 7(3), 334. Klapper, J. T. (1950). The Effects of Mass Media. The Public Opinion Quarterly, 14(2), 342. The National Entertainment State [Electronic (2006). Version]. National Review, 283, 13-30. Retrieved July 8, 2007 from http://search.ebscohost.com/login.aspx?direct=true&db=lgh&AN=21168679&site=ehost-live. Newton, D. E. (1996). Violence and the Media: A Reference Handbook. Santa Barbara, Calif: ABC-CLIO. Niemeyer, G. (1975). Sex and Violence. National Review, 27(29), 834. Olenick, I. (2000). Women’s Exposure to Mass Media is Linked to Attitudes toward Contraception in Pakistan, India and Bangladesh. International Family Planning Perspectives, 26(1), 48. Palmer, E., & Young, B. M. (2003). The Faces of Televisual Media: Teaching, Violence, Selling to Children. Mahwah N.J.: Lawrence Erlbaum Associates, Inc. Sharma, A., & Dlouhy, J. A. (2004). A New Indecency Standard: Lost in ‘Terminal Vagueness’? (Publication. Retrieved July 8, 2007, from CQ Weekly: http://search.ebscohost.com/login.aspx?direct=true&db=lgh&AN=13968623&site=ehost-live Wortzel, L. H., & Frisbie, J. M. (1974). Women’s Role Portrayal Preferences in Advertisements: An Empirical Study. Journal of Marketing, 38(4). Wright, P. (1975). Factors Affecting Cognitive Resistance to Advertising. The Journal of Consumer Research, 2(1), 6.   

Bio-ethics and Genetic Engineering essays

Bio-ethics and Genetic Engineering essays It is my belief that genetic engineering has promise to better mankind, and it is our ethical obligation to research it but not exploit it. There is a need to have a morally correct legislation that guides the way science develops this. The Random House Websters College Dictionary defines bioethics as a field of study and counsel concerned with the implications of certain medical procedures, genetic engineering, and care of the terminally ill. I will be exploring and commenting on how bioethics relates to genetic engineering. Genetic engineering is a branch of biology dealing with the splicing and recombining of genetic units from living organisms, according to Websters New World Dictionary. I will look at bioethics from the point of view of personal privacy, societal effects, religious concerns, medicinal benefits and legislation. The topic of genetic engineering stirs up debates, as it is a controversial area with enormous potential for both good and bad in our society. Genetically prepared drugs have already helped tremendously, in the treament various diseases. Biogenetically prepared vaccines and insulin have already proven their benefit medicine. Other genetically engineered drugs are waiting Federal Drug Administration (FDA) approval. However, critics claim that it will cause more harm than good. Many theologians believe that genetic engineering, should not be investigated at all, they feel Mother Nature knows best and any tampering with genetic material is evil. The primary reason why theologians argue that genetic engineering is unethical is because it defies all that has been described in the story of creation in the bible and other religious texts. However, it is my belief that genetic engineering has promise to better mankind, and it is our ethical obligation to research it but not exploit it. There is a need to have a morally correct legislation that guides the way science develops this (Toward E01.) I...