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. 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 Washington’s 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. Harabagiu. Nonparametric bayesian models for unsupervised event coreference resolution. In NIPS. 2009. [5] E. Benson, A. Haghighi, and R. Barzilay. Event discovery in social media feeds. In ACL, 2011. [6] S. Bethard and J. H. Martin. Identi? cation of event mentions and their semantic class. In EMNLP, 2006. [7] N. Chambers and D. Jurafsky. Template-based information extraction without the templates. In Proceedings of ACL, 2011. [8] N. Chambers, S. Wang, and D. Jurafsky. Classifying temporal relations between events. In ACL, 2007. 9] C. Danescu-Niculescu-Mizil, M. Gamon, and S. Dumais. Mark my words! Linguistic style accommodation in social media. In Proceedings of WWW, pages 745–754, 2011. [10] A. Das Sarma, A. Jain, and C. Yu. Dynamic relationship and event discovery. In WSDM, 2011. [11] G. Doddington, A. Mitchell, M. Przybocki, L. Ramshaw, S. Strassel, and R. Weischedel. The Automatic Content Extraction (ACE) Program–Tasks, Data, and Evaluation. LREC, 2004. [12] T. Dunning. Accurate methods for the statistics of surprise and coincidence. Comput. Linguist. , 1993. [13] J. Eisenstein, B. O’Connor, N. A. Smith, and E. P. Xing.A latent variable model for geographic lexical variation. In EMNLP, 2010. [14] J. Eisenstein, N. A. Smith, and E. P. Xing. Discovering sociolinguistic associations with structured sparsity. In ACL-HLT, 2011. [15] E. Erosheva, S. Fienberg, and J. La? erty. Mixed-membership models of scienti? c publ ications. PNAS, 2004. [16] A. Fader, S. Soderland, and O. Etzioni. Identifying relations for open information extraction. In EMNLP, 2011. [17] J. R. Finkel, T. Grenager, and C. Manning. Incorporating non-local information into information extraction systems by gibbs sampling. In ACL, 2005. [18] E. Gabrilovich, S. Dumais, and E.Horvitz. Newsjunkie: providing personalized newsfeeds via analysis of information novelty. In WWW, 2004. [19] K. Gimpel, N. Schneider, B. O’Connor, D. Das, D. Mills, J. Eisenstein, M. Heilman, D. Yogatama, J. Flanigan, and N. A. Smith. Part-of-speech tagging 10. CONCLUSIONS We have presented a scalable 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 mode ls ? 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. For example: grouping together related entities, classifying entities in relation to their roles in the event, thereby, extracting a frame-based representation of events.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. 8 http://newstimeline. googlelabs. com/ [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] for twitter: Annotation, features, and experiments. In ACL, 2011. T. L. Gri? ths and M. Steyvers. Finding scienti? c topics. Proc Na tl Acad Sci U S A, 101 Suppl 1, 2004. R. Grishman and B. Sundheim. Message understanding conference – 6: A brief history.In Proceedings of the International Conference on Computational Linguistics, 1996. Z. Kozareva and E. Hovy. Learning arguments and supertypes of semantic relations using recursive patterns. In ACL, 2010. G. Kumaran and J. Allan. Text classi? cation and named entities for new event detection. In SIGIR, 2004. J. D. La? erty, A. McCallum, and F. C. N. Pereira. Conditional random ? elds: Probabilistic models for segmenting and labeling sequence data. In ICML, 2001. J. H. Lau, K. Grieser, D. Newman, and T. Baldwin. Automatic labelling of topic models. In ACL, 2011. J.Leskovec, L. Backstrom, and J. Kleinberg. Meme-tracking and the dynamics of the news cycle. In KDD, 2009. W. Lewis, R. Munro, and S. Vogel. Crisis mt: Developing a cookbook for mt in crisis situations. In Proceedings of the Sixth Workshop on Statistical Machine Translation, 2011. C. X. Lin, B. Zhao, Q. Mei, and J. Han. PET: a statistical model for popular events tracking in social communities. In KDD, 2010. J. Lin, R. Snow, and W. Morgan. Smoothing techniques for adaptive online language models: Topic tracking in tweet streams. In KDD, 2011. X. Ling and D. S. Weld.Temporal information extraction. In AAAI, 2010. X. Liu, S. Zhang, F. Wei, and M. Zhou. Recognizing named entities in tweets. In ACL, 2011. I. Mani, M. Verhagen, B. Wellner, C. M. Lee, and J. Pustejovsky. Machine learning of temporal relations. In ACL, 2006. I. Mani and G. Wilson. Robust temporal processing of news. In ACL, 2000. R. C. Moore. On log-likelihood-ratios and the signi? cance of rare events. In EMNLP, 2004. R. Munro. Subword and spatiotemporal models for identifying actionable information in Haitian Kreyol. In CoNLL, 2011. G. Neubig, Y. Matsubayashi, M. Hagiwara, and K.Murakami. Safety information mining – what can NLP do in a disaster -. In IJCNLP, 2011. D. Newman, A. U. Asuncion, P. Smyth, and M. Welling. Distributed inference for latent dirichlet allocation. In NIPS, 2007. D. Newman, J. H. Lau, K. Grieser, and T. Baldwin. Automatic evaluation of topic coherence. In HLT-NAACL, 2010. ? e D. O S? aghdha. Latent variable models of selectional preference. In ACL, ACL ’10, 2010. S. Petrovi? , M. Osborne, and V. Lavrenko. Streaming c ? rst story detection with application to twitter. In HLT-NAACL, 2010. [41] A. -M. Popescu and M. Pennacchiotti.Dancing with the stars, nba games, politics: An exploration of twitter users’ response to events. In ICWSM, 2011. [42] A. -M. Popescu, M. Pennacchiotti, and D. A. Paranjpe. Extracting events and event descriptions from twitter. In WWW, 2011. [43] J. Pustejovsky, P. Hanks, R. Sauri, A. See, R. Gaizauskas, A. Setzer, D. Radev, B. Sundheim, D. Day, L. Ferro, and M. Lazo. The TIMEBANK corpus. In Proceedings of Corpus Linguistics 2003, 2003. [44] A. Ritter, C. Cherry, and B. Dolan. Unsupervised modeling of twitter conversations. In HLT-NAACL, 2010. [45] A. Ritter, C. Cherry, and W. B. Dolan.Data-driven response generation in social media. In EMNLP, 2011. [46] A. Ritter, S. Clark, Mausam, and O. Etzioni. Named entity recognition in tweets: An experimental study. EMNLP, 2011. [47] A. Ritter, Mausam, and O. Etzioni. A latent dirichlet allocation method for selectional preferences. In ACL, 2010. [48] K. Roberts and S. M. Harabagiu. Unsupervised learning of selectional restrictions and detection of argument coercions. In EMNLP, 2011. [49] T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes twitter users: real-time event detection by social sensors. In WWW, 2010. [50] R. Saur? R.Knippen, M. Verhagen, and ? , J. Pustejovsky. Evita: a robust event recognizer for qa systems. In HLT-EMNLP, 2005. [51] F. Song and R. Cohen. Tense interpretation in the context of narrative. In Proceedings of the ninth National conference on Arti? cial intelligence – Volume 1, AAAI’91, 1991. [52] B. Van Durme and D. Gildea . Topic models for corpus-centric knowledge generalization. In Technical Report TR-946, Department of Computer Science, University of Rochester, Rochester, 2009. [53] D. S. Weld, R. Ho? mann, and F. Wu. Using wikipedia to bootstrap open information extraction. SIGMOD Rec. , 2009. 54] Y. Yang, T. Pierce, and J. Carbonell. A study of retrospective and on-line event detection. In Proceedings of the 21st 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.

Typical Course of Study - Kindergarten

The elementary years lay the foundation for learning throughout a students educational career (and beyond). Childrens abilities undergo dramatic changes from kindergarten through 5th grade.   While public and private schools set the standards for their students,  homeschooling parents  may be unsure what to teach at each grade level. Thats where a typical course of study comes in handy.   A typical course of study provides a general framework for introducing appropriate skills and concepts for each subject at each grade level. Parents may notice that some skills and topics are repeated in multiple grade levels. This repetition is normal because the complexity of skills and depth of topics increases as a students ability and maturity increases. Kindergarten Kindergarten is a highly-anticipated time of transition for most children. Learning through play starts to give way to more formal lessons. (Though play remains an essential part of education through the elementary years.) For most young children, this first foray into formal learning will include pre-reading and early math activities. It is also a time for children to begin understanding their role and the roles of others in the community.   Language Arts A typical course of study for kindergarten language arts includes pre-reading activities such as learning to recognize upper- and lower-case letters of the alphabet and the sounds of each. Children enjoy looking at picture books and pretending to read. Its crucial to read to kindergarten students on a regular basis. Not only does reading aloud help children make connections between written and spoken words, but it also helps them acquire new vocabulary skills. Students should practice writing the letters of the alphabet and learn to write their name. Children may use drawings or invented spelling to tell stories.   Science Science helps kindergarten students begin to understand the world around them. It is essential to provide opportunities for them to explore science-related topics through observation and investigation. Ask students questions such as how, why, what if, and what do you think. Use nature study to help young students explore earth science and physical science. Common topics for kindergarten science include insects, animals, plants, weather, soil, and rocks.   Social Studies In kindergarten, social studies focus on exploring the world through the local community. Provide opportunities for children to  learn about themselves and their role in their family and community. Teach them about community helpers such as police officers and firefighters.   Introduce them to basic facts about their country, such as its president, its capital city, and some of its national holidays. Help them explore basic geography with simple maps of their home, city, state, and country. Math A typical course of study for kindergarten math includes topics such as counting, number recognition, one-to-one correspondence, sorting and categorizing, learning basic shapes, and pattern recognition. Children will learn to recognize numbers 1 through 100 and count by ones to 20. They will learn to describe the position of an object such as in, beside, behind, and between.   They will learn to recognize simple patterns such as A-B (red/blue/red/blue), complete a pattern that has been started for them, and create their own simple patterns. First Grade Children in first grade are starting to acquire more abstract thinking skills. Some begin to move toward reading fluency. They can understand more abstract math concepts and can complete simple addition and subtraction problems. They are becoming more independent and self-sufficient. Language Arts A typical course of study for first-grade language arts introduces students to age-appropriate grammar, spelling, and writing. Children learn to capitalize and punctuate sentences correctly. They are expected to spell grade level words correctly and capitalize common nouns. Most first grade students will learn to read one-syllable words that follow general spelling rules and use phonics skills to decipher unknown words.  Ã‚   Some common skills for first graders include using and understanding compound words; inferring a words meaning from context; understanding figurative language;  and writing short compositions. Science First-grade students will build on the concepts they learned in kindergarten. They will continue asking questions and predicting outcomes and will learn to find patterns in the natural world. Common science topics for first grade include plants; animals; states of matter (solid, liquid, gas); sound; energy; seasons; water; and weather. Social Studies First-grade students can understand the past, present, and future, though most dont have a solid grasp of time intervals (for example, 10 years ago vs.  50 years ago). They understand the world around them from the context of the familiar, such as their school and community.   Common first-grade social studies topics include basic economics (needs vs. wants), beginning  map skills (cardinal directions and locating state and country on a map), continents, cultures, and national symbols. Math First-grade math concepts reflect this age groups improved ability to think abstractly. Skills and concepts typically taught include addition and subtraction;  telling time to the half-hour; recognizing and counting money; skip counting (counting by 2s, 5s, and 10s); measuring;  ordinal numbers (first, second, third); and naming and drawing two-dimensional and three-dimensional shapes. Second Grade Second-grade students are becoming better at processing information and can understand more abstract concepts. They understand jokes, riddles, and sarcasm and like to try them on others.   Most students who did not master reading fluency in first grade will do so in second. Most second graders have also established foundational writing skills. Language Arts A typical course of study for second-grade children focuses on reading fluency. Children will begin reading grade-level text without stopping to sound out most words. They will learn to read orally at a conversational speaking rate and use  voice inflection for expression. Second-grade students will learn  more complex phonics concepts and vocabulary. They will begin to learn prefixes, suffixes, antonyms, homonyms, and synonyms. They may start learning cursive handwriting.  Ã‚   Common skills for second-grade writing include using reference tools (such as a dictionary); writing opinion and how-to compositions; using planning tools such as brainstorming and graphic organizers; and learning to self-edit. Science In second grade, children begin using what they know to make predictions (hypothesis) and look for patterns in nature. Common second-grade life science topics include life cycles, food chains, and habitats (or biomes).   Earth science  topics include the Earth and how it changes over time; the factors affecting those changes such as wind, water, and ice; and the physical properties and classification of rocks.   Students are also introduced to force and motion concepts such as push, pull, and  magnetism. Social Studies Second graders are ready to begin moving beyond their local community and using what they know to compare their region with other areas and cultures.   Common topics include Native Americans, key historical figures (such as George Washington or Abraham Lincoln), creating timelines, the United States Constitution, and the election process. Second graders will also learn more advanced map skills, such as locating the United States and individual states; finding and labeling oceans, continents, the North and South Poles, and the equator. Math In second grade, students will begin to learn more complex math skills and attain fluency in math vocabulary.   A second-grade math course of study usually includes place value (ones, tens, hundreds); odd and even numbers; adding and subtracting two-digit numbers; introduction of multiplication tables; telling time from the quarter hour  to the  minute; and fractions. Third Grade In third grade, students begin to make the shift from guided learning to more independent exploration. Because most third-graders are fluent readers, they can read directions themselves and take more responsibility for their work. Language Arts In language arts, the focus on reading shifts from learning to read to reading to learn. There is an emphasis on reading comprehension. Students will learn to identify the main idea or moral of a story and be able to describe the plot and how the actions of the main characters affect the plot. Third graders will begin using more complex graphic organizers as part of the pre-writing process. They will learn  to write book reports, poems, and personal narratives. Topics for third-grade grammar include parts of speech; conjunctions; comparative and superlatives; more complex capitalization and punctuation skills (such as capitalizing book titles and punctuating dialogue); and sentence types (declarative, interrogative, and exclamatory).   Students also learn about writing genres such as fairy tales, myths, fiction, and biographies.   Science Third graders start to tackle more complex science topics. Students learn about the scientific process,  simple machines  and  the moon and its phases. Other topics include living organisms (vertebrate and invertebrates); properties of matter; physical changes; light and sound; astronomy; and inherited traits. Social Studies Third-grade social studies topics help students continue to expand their view of the world around them. They learn about cultures and how the environment and physical features affect the people of a given region. Students learn about topics such as transportation, communication, and the exploration and colonization of North American. Geography topics include latitude, longitude, map scale, and geographic terms. Math Third-grade mathematical concepts continue to increase in complexity.   Topics include multiplication and division; estimation; fractions and decimals; commutative and associative properties; congruent shapes, area and perimeter; charts and graphs; and probability.   Fourth Grade Most fourth-grade students are ready to tackle more complex work independently. They start learning basic time management and planning techniques for long-term projects. Fourth-graders are also starting to discover their academic strengths, weaknesses, and preferences. They may be asynchronous learners who dive into topics that interest them while struggling in areas that dont.   Language Arts Most fourth-grade students are competent, fluent readers. It is an excellent time to introduce books series since many children at this age are captivated by them.   A typical course of study includes grammar, composition, spelling, vocabulary-building, and literature. Grammar focuses on topics such as similes and metaphors; prepositional phrases; and run-on sentences.   Composition topics include creative, expository, and persuasive writing; research (using sources such as the internet, books, magazines, and news reports); understanding fact vs. opinion; point of view; and editing and publishing. Students will read and respond to a variety of literature. They will explore genres such as folklore, poetry, and tales from a variety of cultures.   Science Fourth-grade students continue to deepen their understanding of the scientific process through practice. They may try conducting age-appropriate experiments and document them by writing lab reports.  Ã‚   Earth science topics in fourth grade include natural disasters (such as earthquakes and volcanoes); the solar system; and natural resources. Physical science topics include electricity and electrical currents; physical and chemical changes in states of matter (freezing, melting, evaporation, and condensation); and the water cycle. Life science topics typically cover how plants and animals interact with and support one another (food chains and food webs), how plants produce food, and how humans impact the environment. Social Studies The history of the United States and the students home state are common topics for social studies in fourth grade. Students will research facts about their home states such as its native population, who settled the land, its path to statehood, and significant people and events from state history.   U.S. history topics include the Revolutionary War and westward expansion (the explorations of Lewis and Clark and the lives of American pioneers) Math Most fourth-grade students should be comfortable adding, subtracting, multiplying, and dividing quickly and accurately. They will apply these skills to large whole numbers and learn to add and subtract fractions and decimals.   Other fourth-grade math skills and concepts include prime numbers; multiples; conversions; adding and subtracting with variables; units of metric measurements; finding the area and perimeter of a solid; and figuring the volume of a solid. New concepts in geometry include lines, line segments, rays, parallel lines, angles, and triangles.   Fifth Grade Fifth grade is the last year as an elementary student for most students since middle school is generally considered grades 6-8. While these young tweens may consider themselves mature and responsible, they often need continued guidance as they prepare to transition fully to independent learners.   Language Arts A typical course of study for fifth-grade language arts will include components that become standard through the high school years: grammar, composition, literature, spelling, and vocabulary-building.   The literature component includes reading a variety of books and genres; analyzing plot, character, and setting; and identifying the authors purpose for writing and how his point of view influences his writing. Grammar and composition focus on using correct age-appropriate grammar to write more complex compositions such as letters, research papers, persuasive essays, and stories; honing pre-writing techniques such as brainstorming and using graphic organizers; and building on the students understanding of parts of speech and how each is used in a sentence (examples include prepositions, interjections, and conjunctions). Science Fifth graders have a strong basic understanding of science and the scientific process. Theyll put those skills to work as they delve into a more complex understanding of the world around them. Science topics usually covered in fifth grade include the solar system; the universe; Earths atmosphere; healthy habits (proper nutrition and personal hygiene); atoms, molecules, and cells; matter; the Periodic Table; and taxonomy and the classification system. Social Studies In fifth grade, students continue their exploration of American history, studying events such as the War of 1812; the American Civil War; inventors and technological advances of the 19th century (such as Samuel B. Morse, the Wright Brothers, Thomas Edison, and Alexander Graham Bell); and basic economics (the law of supply and demand; the primary resources, industries, and products of the United States and other countries). Math A typical course of study for fifth-grade math  include dividing two- and three-digit whole numbers with and without remainders; multiplying and dividing fractions; mixed numbers; improper fractions; simplifying fractions; using equivalent fractions; formulas for area, perimeter, and volume; graphing; Roman numerals; and powers of ten. This typical course of study for elementary school is intended as a general guide. The introduction of topics and acquisition  of skills can vary widely based on the studentss maturity and ability level, a familys preferred homeschooling style, and the type of homeschool curriculum used.

Organic Food Is Better Than Conventional Food - 940 Words

Organic food is a current topic in today’s healthful world. There are different sides to the organic food argument. One is that organic food is much better than conventional food. The other is that conventional food is just as good as organic and more for your dollar. To some families organic food is more then they can afford because of the extra work that is required to grow it. People say that organic food is better because it has no chemicals or fertilizer in it but that is not true because they do put fertilizer on it just â€Å"natural† fertilizer that is certified by the USDA. Conventional food which to many people think is not safe because of the chemicals in it but it is just as safe as organic food. Some organic food is not completely chemical free. Certified organic is the most chemical free but not completely. According to the Mayo Clinic if the produce has a USDA organic seal on it then it is 95 to 100 percent organic. â€Å"Products that are completely or ganic — such as fruits, vegetables, eggs or other single-ingredient foods — are labeled 100 percent organic and can carry the USDA seal. Foods that have more than one ingredient, such as breakfast cereal, can use the USDA organic seal plus the following wording, depending on the number of organic ingredients, 100 percent organic. To use this phrase, products must be either completely organic or made of all organic ingredients. Organic. Products must be at least 95 percent organic to use this term† (Are They Safe?).Show MoreRelatedOrganic food has better ratings on health benefits than conventional food but conventional food1300 Words   |  6 PagesOrganic food has better ratings on health benefits than conventional food but conventional food costs less. Most people have a hard time making an educated decision on the better selection. 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Commercial Advertisement Coca Cola Make It Happy

Commercial Advertisement: Coca-Cola-Make It Happy Mood The mood depicted in the commercial is that of positivity, happiness, and optimism that we should put our variations aside so that we can all be victorious. Optimism is, therefore, depicted as it is clear that every living thing in the universe deserves nothing than the best and human beings are not an exemption. From the mood shown it becomes clear that living things should treat others as neighbors, and this is where the virtue of unity emerges. The virtue of happiness can be viewed based on the fact that at the end of the advert the insects are successful in terms of accomplishing their set goal. The set goal at this point is ensuring that the coke bottle is opened so that each one of them benefits in one way or another. The insects are, therefore, able to achieve this through the virtue of cooperation that they incorporate. On the other hand, a mood of surprise can be depicted based on the fact that the coke bottle that the insects take belongs to a man who was laying on the ground taking a nap. After the man wakes up, he is shocked that his coke bottle is missing, and he is not able to comprehend what took place when he was a sleep. It is, therefore, evident that different moods were noticed in the advertisement, and this is instrumental in ensuring that the commercial attains its intended purpose. Soundtrack The music used is cool and soothing, and this is ideal for making the audience eager about what theShow MoreRelatedCoca Cola s Big Game Commercial Appeals1111 Words   |  5 PagesIs there such thing as an advertisement that could turn someone’s day completely around? Advertisements are used by companies every day to persuade viewers or make them feel a certain way about a certain situation. 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Quiz Questions for Chapter 9 Free Essays

Quiz Questions for Chapter 9 1. A truck was purchased for $25,000. It has a six-year life and a $4,000 salvage value. We will write a custom essay sample on Quiz Questions for Chapter 9 or any similar topic only for you Order Now Using straight-line depreciation, what is the asset’s carrying value (book value) after 2 1/2 years? a. $8,750. b. $12,250. c. $14,583. d. $16,250. 2. On January 1, 2003, Superior Landscaping Company paid $17,000 to buy a stump grinder. If Superior uses the grinder to remove 2,500 stumps per year, it would have an estimated useful life of 10 years and a salvage value of $4,500. The amount of depreciation expense for the year 2003, using units-of-production depreciation and assuming that 3,500 stumps were removed, is a. 2,380. b. $1,750. c. $1,700. d. $1,250. 3. The sale for $2,000 of equipment that cost $8,000 and has accumulated depreciation of $6,700 would result in a a. gain of $2,000. b. gain of $700. c. loss of $700. d. loss of $1,300. 4. Underestimating the number of tons of a mineral that can be mined over a mineral deposit’s life will result in a. overstated net income each year. b. overstated total assets each year. c. overstated depletion expense each year. d . no effect on total assets each year. 5. A copyright is obtained for what becomes a very successful book. The publisher expects the book to generate sales for 10 years. The copyright should be amortized over a. 2 to 4 years. b. 10 years. c. 40 years. d. the author’s life plus 50 years. The following information pertains to the next two questions. Z Company purchased an asset for $24,000 on January 1, 2004. The asset was expected to have a four-year life and a $4,000 salvage value. 6. The amount of depreciation expense for 2006 using double-declining-balance would be a. $2,000. b. $3,000. c. $6,000. d. $12,000. 7. Assume that Z Company uses straight-line depreciation. If on January 1, 2007, Z Company sells the asset for $10,000, the statement of cash flows would report a a. $1,000 cash inflow from gain on the sale of the asset in the operating activities section. b. $10,000 cash inflow from an asset disposal in the investing activities section. c. $9,000 cash inflow from an asset disposal in the financing activities section. d. a and c. 8. On January 1, 2006, Fulsom Corporation purchased a machine for $50,000. Fulsom paid shipping expenses of $500 as well as installation costs of $1,200. Fulsom estimated the machine would have a useful life of ten years and an estimated salvage value of $3,000. If Fulsom records depreciation using the straight-line method, depreciation expense for 2007 is. a. $4,870. b. $5,170. c. $5,270. d. $5,570. 9. Hickory Ridge Company purchased land and a building for $920,000. The individual assets were appraised at the following market values: Land $614,400 Building $345,600 Recording the land in the accounting records would a. increase land by $588,800. b. increase land by $614,400. c. increase assets by $920,000. d. Both a and c. 10 Penny Lane and Associates purchased a generator on January 1, 2006, for $6,300. The generator was estimated to have a five-year life and a salvage value of $600. At the beginning of 2008, the company revised the expected life of the asset to six years and revised the salvage value to $300. Using straight-line depreciation, the depreciation expense recorded in 2008 would a. decrease assets and equity by $1,140. b. decrease assets and equity by $930. c. decrease assets and equity by $1,005. d. decrease assets and equity by $1,500. 11 Which of the following statements about goodwill is true? a. The amount of goodwill is measured by subtracting the amount paid for assets from their fair market value on the purchase date. b. The amount of goodwill is recorded as an asset. . Recording impairment of goodwill reduces the amount of net income. d. All of the above. 12 XYZ Company paid cash for a capital expenditure that improved the operating efficiency of one of its assets. Which of the following reflects how this expenditure affects the company’s financial statements? a. b. c. d. 13 Assets = +- +- – n/a Liab. n/a n/a n/a n/a + Equity n/ a n/a – n/a Rev. – n/a n/a n/a n/a Exp. n/a n/a + n/a = Net Inc. n/a n/a – n/a Cash Flow – IA n/a – OA n/a KLM Company experienced an accounting event that affected its financial statements as indicated below: Assets = – Liab. n/a Equity – Rev. – n/a Exp. + = Net Inc. – Which of the following events could have caused these effects? a. recognizing depreciation. b. paying cash for a capital expenditure. c. amortizing a patent. d. none of the above. Cash Flow – OA 14. Which of the following correctly matches the type of long-term asset with the term used to identify how that asset’s cost is expensed? Building Oil Reserve Copyright a. Amortization Depreciation Depletion b. Depletion Amortization Depletion c. Amortization Depletion Depreciation d. Depreciation Depletion Amortization 15. Which of the following is true? . The book value of an asset is its estimated market value. b. The primary purpose of recording depreciation expense on the income statement is to reduce income tax expense. c. Recording depreciation expense decreases the book value of the asset in the year it was used to produce revenue. d. The accumulated deprecation for an asset provides the cash needed to replace the asset at the end of its useful life. Quiz Questions for Chapter 10 The following information pertains to the next seven questions. On January 1, 2003, XYZ Corporation issued a $5,000 face value bond that sold for 90. The bond had a five-year term and paid 10 percent annual interest. The company used the proceeds from the bond issue to buy land. The land was leased for $600 of cash revenue per year and was sold at the end of the 5th year for $4,200 cash. 1. The carrying value of the bond liability on January 1, 2003, would be a. $4,600. b. $4,500. c. $5,000. d. $4,000. 2. The amount of interest expense reported on the 2003 income statement would be a. $450. b. $400. c. $500. d. $600. 3. Interest expense reported on the income statement over the life of the bond would a. ncrease by $100 each year. b. decrease by $100 each year. c. be the same each year. d. equal the stated rate of interest. 4. The carrying value of the bond liability on December 31, 2007 would be a. $4,500. b. $5,000. c. $4,900. d. $4,600. 5. The sale of the land on December 31, 2007, would a. increase retained earnings by $300. b. increase equity by $4,200. c. reduce net income by $300. d. have no effect on retained earnings. 6. T he total amount of liability associated with the bond issue would a. increase each year as a result of the amortization of the discount. b. ecrease each year as a result of the amortization of the discount. c. remain the same each year. d. always be equal to the face value of the bond payable. 7. The amount of the cash outflow for interest expense in 2005 would be a. $600. b. $400. c. $500. d. $ 0. Use the following information to answer the next three questions. On January 1, 2003 , Keynes Company issued a $20,000 face value bond that sold for 110. The bond had a ten-year term and a stated annual interest rate of 8 percent . 8. The carrying value of the bond liability on January 1, 2003, would be a. $20,000. . $22,000. c. $21,800. d. $20,200. 9. The amount of interest expense reported on the company’s 2003 income statement would be a. $1,200. b. $1,400. c. $1,600. d. $1,050. 10. The amount of interest expense reported on the company’s 2004 income statement would be a. $1,400. b. $1,600. c. $1,800. d. $2,000. 11. If a bond sells at a discount, which of the following is true? a. The market interest rate at the time of issue is greater than the stated interest rate on the bond. b. The market interest rate at the time of issue is less than the stated interest rate on the bond. c. The market interest rate at the time of issue is the same as the stated interest rate on the bond issue. d. The market interest rate is expected to increase above the stated interest rate on the bond. 12. On January 1, 2003, Ink, Inc. borrowed $100,000 cash from the Fidelity Bank on a note that had a 6 percent annual interest rate and a five-year term. The loan is to be repaid in annual payments of $23,741. 69 on January 1 each year. The amount of the January 1, 2004, payment applied to interest and to principal would be a. $6,000 / $94,000. b. $17,741. 69 / $94,000. c. $4,935. 0 / $82,258. 31. d. $6,000 / $17,741. 69. 13. Indigo Company can borrow up to $50,000 on its line of credit at the state bank. The company agrees to pay interest monthly at 2 percent above prime. Funds are borrowed or repaid on the first day of each month. Month Jan. Feb. March Amounts Borrowed or (Repaid) $15,000 $ (5,000) $30,000 Prime Rate 6 percent 5 percent 4 percent The amount of interest to be accrued o n the March 31 is a. $225. 00. b. $100. 00. c. $133. 33. d. $200. 00. 14. XYZ Company experienced an accounting event that affected its financial statements as indicated below: Assets = Liab. + + Equity n/a Rev. – n/a Exp. n/a = Net Inc. n/a Cash Flow + FA Which of the following events could have caused these effects? a. A bond issued at face value. b. A bond issued at a discount. c. A bond issued at a premium. d. All of the above. 15. A bond will sell at a premium if: a. The market rate of interest is equal to the bond’s stated rate. b. The market rate of interest is greater than the bond’s stated rate. c. The market rate of interest is less than the bond’s stated rate. d. The bond is convertible into common stock. Quiz Questions for Chapter 11 1. The ZZ Corporation had the following shares of stock outstanding at December 31, 2003: Common Stock, $50 par value, 40,000 shares outstanding; and Preferred Stock, 6 percent, $100 par value, cumulative, 10,000 shares outstanding. Dividends for 2001 and 2002 were in arrears. On December 31, 2003, ZZ declared total cash dividends of $250,000. The total amounts payable to preferred stockholders and common stockholders, respectively, are: a. $60,000 / $190,000. b. $120,000 / $130,000. c. $125,000 / $125,000. d. $180,000 / $70,000. Use the following information to answer the next four questions. The Kramer Company was started when it issued 200 shares of $5 par value common stock at a market price of $20 per share. The company repurchased 10 shares at a market price of $15 per share. Later the company reissued 5 shares at a market price of $20 per share. At the end of the first year of operations the company’s equity included $1,200 of retained earnings in addition to its contributed capital. 2. The entry to record the original issue of 200 shares of stock would a. increase cash by $4,000 / increase common stock by $4,000. b. ncrease cash by $4,000 / increase common stock and paid-in capital in excess of par value by $1,000 and $3,000, respectively. c. decrease cash by $4,000 / increase common stock common stock by $4,000. d. increase cash by $1,000 / increase common stock by $1,000. 3. The entry to record the purchase of the 10 shares of the company’s own stock would a. decrease assets / decrease equity. b. decrease assets / increase equity. c. decrease assets / increase treasury stock. d. both a and c. 4. What effect would reissuing the 5 shares have on the company’s paid-in capital from treasury stock transactions account? . No effect. b. Increase additional paid-in capital by $100. c. Increase additional paid-in capital by $25. d. Decrease additional paid-in capital by $75. 5. The total amount of stockholders’ equity at the end of the first year would be a. $5,150. b. $5,200. c. $1,200. d. none of the above. 6. Which of the following is an advantage of the corporate form of business organization? a. double taxation. b. amount of regulation. c. limited liability. d. entrenched management. 7. Jan Irving started a proprietorship on January 1, 2007 with a $1,000 cash contribution to the business. During the first year of operations the company generated $5,000 of cash revenue and incurred $2,000 of cash expenses. Also, Jan withdrew $500 from the business. At the end of 2007 the balance in the Jan Irving, Capital account was a. $1,000. b. $3,000. c. $3,500. d. $4,000. 8. ABC Company is authorized to issue 100,000 shares of common stock. The company issued 60,000 shares of common stock and later repurchased 15,000 shares of its own common stock. How many shares are outstanding? a. 60,000. b. 45,000. c. 100,000. d. 40,000. 9. An 8 percent stock dividend on 12,000 shares of outstanding preferred stock with a par value of $20 per share and a market value of $60 a share will have what effect on the accounting equation? a. Increase preferred stock by $57,600. b. Increase cash by $38,400. c. Decrease retained earnings by $19,200. d. Decrease retained earnings by $57,600. 10. Which of the following statements concerning a two-for-one stock split is true? a. The number of shares outstanding will decrease. b. The market price of the stock would be expected to increase. c. The company’s equity will increase. d. No journal entry would be necessary. 1. EFG Company paid cash to purchase treasury stock. Which of the following reflects how this event affects the company’s financial statements? a. b. c. d. 12. Assets – +- – +- = Liab. n/a n/a n/a n/a + Equity – n/a – n/a Rev. – n/a n/a n/a n/a Exp. n/a n/a + + = Net Inc. n/a n/a – – Cash Flow – FA â⠂¬â€œ OA – FA – OA ZGAR Company distributed a stock dividend. Which of the following reflects how this event affects the company’s financial statements? a. b. c. d. Assets – n/a – n/a = Liab. n/a n/a n/a n/a + Equity – +- – +- Rev. – n/a n/a n/a n/a Exp. n/a n/a + n/a = Net Inc. n/a n/a – n/a Cash Flow n/a n/a – FA – FA Quiz for Chapter 12 1. Which of the following cash transactions is classified as an investing activity on the statement of cash flows? a. Cash borrowed. b. Cash received from issuing stock. c. Cash received from revenue. d. Cash collected on a loan. 2. A building costing $55,000 with $16,500 of accumulated depreciation was sold for $40,000. How would the cash flow from the sale appear on the statement of cash flows? a. $1,500 in operating activities and $38,500 in investing activities. b. $40,000 in financing activities. c. $38,500 noncash financing and investing activities and $1,500 in operating activities. . $40,000 in investing activities. 3. The owners of X Company invested $2,000 in the company. X Company used the cash to invest in Y Company. On X’s statement of cash flows these transactions would be classified, respectively, as a. an investing activity and an investing activity. b. a financing activity and a financing activity. c. an investing activity and a financing a ctivity. d. a financing activity and an investing activity. 4. Issuing a note for the purchase of land is an example of a. an investing activity. b. a financing activity. c. a noncash investing and financing activity. d. transaction that would not appear on the statement of cash flows. 5. The sum of the three major components (operating activities, investing activities, and financing activities) on a statement of cash flows will add up to a. the ending cash balance. b. the change in the cash account balance between the beginning and ending of the period. c. the amount of cash inflow for the period. d. net income for the period. Answers: Chapter 9: D, B, B, C, B, A, B, A, D, B, D, A, D, D, C Chapter 10: B, D, C, B, C, A, C, B, B, A, A, D, D, D, C Chapter 11: D, B, D, C, A, C, C, B, D, D, A, B Chapter 12: D, D, D, C, B How to cite Quiz Questions for Chapter 9, Essay examples

Difference Between Microeconomics and Macroeconomics free essay sample

There are differences between microeconomics and macroeconomics, although, at times, it may be hard to separate the functions of the two. Microeconomics and macroeconomics are the two major categories within the field of economics. Microeconomics is the branch of economy, especially such topics as markets, prices, industries, demand, and supply. Microeconomic concentrates on the difficulties of the markets for services and goods, and how the price affects the growth of the markets (Microeconomics 2000-2010). Microeconomics examines the behavior of individual economics entities: firms and consumers. How do individuals make consumption decisions? How do firms make profits and price their goods and services. The main focus of microeconomics is markets, wage markets, the market for gasoline, rent markets, etc. Macroeconomics is the branch of economics that studies the entire economy, especially topics as aggregate production, unemployment, inflation, and business cycles. Macroeconomics is a vast field that concentrates on two areas, economic growth and changes in the national income (Macroeconomics 2000-2010). We will write a custom essay sample on Difference Between Microeconomics and Macroeconomics or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page Macroeconomics asks questions like; why does the U. S. conomy generally experience higher rates of growth than European economies? What causes inflation? What effect does the national debt have on economic growth? Economic goals have five conditions of the mixed economy, including full employment, stability, economic growth, efficiency, and equity, that are generally desired by society and pursued by governments through economic policies. The five goals are typically divided into the two that are important for microeconomics (efficiency and equity) and the three that are important for macroeconomics (full employment, stability, and economic growth) (Economic Goals 2000-2010). Efficiency and equity are the two-microeconomics goals most relevant to markets, industries, and part of the economy and are thus important to the study of microeconomics. Efficiency is achieved when society is able to get the greatest amount of satisfaction from available resources. Equity is achieved when income and wealth are fairly distributed within society (Economic Goals 2000-2010). Full employment, stability, and economic growth are the three macroeconomic goals most relevant to the aggregate economy and consequently are of the prime importance to the study of macroeconomics. Full employment is achieved when all available resources labor, capital, land, and entrepreneurship are used to produce goods and services. Stability is achieved by avoiding or limiting fluctuations in production, employment, and prices. Economic growth is achieved by increasing the economys ability to produce goods and services (Economic Goals 2000-2010). Each goal, achieved by itself, improves the overall well being of society. Examples of Microeconomics and Macroeconomics Microeconomics, focus on the supply and demand side, which refers to the behavior of people as they interact with one another in competitive market (Mankiw 2009). When a national disaster such as a hurricane hits a region, basic commodities, such as gasoline and bottled water experience increasing demand and shrinking supplies. Even with the cost of gasoline going up every week, the demand is still very high. With the supply of gasoline going down the cost is getting higher just to keep up with the demand of gasoline. I spend about 40 dollars a week to keep gasoline in my car so I can get back and forth to work and to do me daily errands. Macroeconomics focus on changes in employment rates, inflation, and interest rates. Loans were made, by banks and mortgage companies like Freddie Mac and Fannie Mae to people who simply could not afford to repay them back. When the mortgage crisis was happen I went to apply for a loan to get a house so I could get the 8,000 house credit I was turn away even with me having a good job and being on my job for over 3 years. They only wanted to give out loans to people who had a 770 credit score or higher. My credit score is not bad around 685 but in the recent year, I just went through a divorce that had a very negative effect on my credit score. With all the economic downfalls, this has affected my family and me very much. I have to work harder so I can provide food for my son and childcare keep going up just to keep up with the rises in prices for goods. As a single mother lately, I have to budget a lot and cut out many fun actives just because I can no longer afford them. Reference: Macroeconomics, Amos Web Encyclonomic WEB*pedia, Retrieved October 19, 2010 from http://www. AmosWEB. com, Amos WEB LLC, 2000-2010 Microeconomics, Amos Web Encyclonomic WEB*pedia, Retrieved October 19, 2010 from http://www. AmosWEB. com, Amos WEB LLC, 2000-2010 Economic Goals, Amos Web Encyclonomic WEB*pedia, Retrieved October 20, 2010 from http://www. AmosWEB. com, Amos WEB LLC, 2000-2010 Mankiw N. Gregory. Principles of Economics (fifth edition) South-Western Cengage Learning