Negative Impacts of Information Technology

Negative Impacts of Information Technology THE NEGATIVE EFFECTS OF INFORMATION TECHNOLOGY ON SOCIETY Technology is the application of knowledge to the practical aims of human life or to change and manipulate the human environment. In this century, technological advancement has made our lives easier and more comfortable. We enjoy higher incomes and better standard of living as a result of progress and development, but rapid advancement of technology has impacted our society globally. According to Power 22, â€Å"In 2008, just 16 percent of world’s working population qualified as hyper-connected, but the study predicted that 40 percent of us would soon meet the criteria.† Technology has always improved from time to time, and there will be more people rely on technology. In the future, technology will be replacing many things and people’s life will be easier. However, many people are seeing the benefits of technology only and never look at its impacts. By the way, excessive usages of technology will effects people’s daily life. There are many negative effec ts of technology which will effects people’s daily life such as language proficiency, social life and health. Although technology has helped us in many fields, but there are still many people do not realise the fact that technology has negative affects society. The first main point for the negative impacts of information technology on society is poor language proficiency. Language proficiency is the ability of an individual to speak or perform in an acquired language. This is a very serious matter to be concern about this developing information technology on society. This is because the modern technology allows the students to communicate with their families and associates instantly using application such as Line, WeChat and WhatsApp. This application will make life easier to communicate between each other. However, this will cause them to ignore the spelling of different words and the usage of proper grammar. Furthermore, with the increasing amount of information on the web, Internet users may come across inaccurate information and lead to misinformation or even slightly skewed way of thinking. This may confused the student understanding through some information. Students in this generation love to communicate with their friends and tend t o make new friends when they are on social networks. However, some of them will face problems when it comes to real world face-to-face communication. They wouldn’t know the differences between communication in social networks and also when they communicate face-to-face. Moreover, they will have problems in communication because they could not pronounce different words correctly. According to Erica Loop (2014) â€Å"As an adult, you may know that Mr. Bob’s bio facts are far from true, but that doesn’t mean that your child has the same understanding.† With the poor language proficiency, one might misunderstand the information available on the web. They have to know the good and bad of our technology in this society. As a conclusion, technology does helps people in learning but people misused it in a wrong way. We shall appreciate our technology in this generation and use it wisely but not getting affected into it and have problems in language proficiency. T o avoid these problems, we have to lessen the communication on social networks and try to communicate with people around us more often. Besides, technological improvement will cause a huge impact on social life. This is because consumers rely on communication devices such as smart phone, I-pad, I-pod, Tab for most of their daily tasks. This causes them neglect quality time with their family members as they are busy trying out the new gadgets or new applications available in the market or getting updated to the current trend on the social networks. For example, nowadays teenagers will keep looking and pressing the screen or button on their communication device while they do activities such as eating, watching TV with their family. Sometimes, they pay more attentions to their devices than to their family. The more advanced technology becomes, the more it seems to have control over our lives. Technology has changed human experience nowadays. Nowadays, people spend more time online than ever before and their social life is affected by internet. They like to read the news from the internet instead of newspaper. Also, they also like to chat by using their devices rather than facing each other. This is because they feel that it will save time and money, but this will cause them to be addicted to technology. The addiction comes from not realising that they already found are what they were looking for. According to Siege, 22, â€Å"the internet has radically changed nearly every level of human experience in an incredibly short amount of time.† With advanced technology, human tend to rely on technology devices in short time. Moreover, the revolution made many previously impossible things that include accessing personal data and information of Internet users that they might never meet. Through the internet, people could post and share links, statuses, pictures, comments and even vent their feelings to other internet users. They can also look through other users’ personal information which would include vital information such as age, birthday, and marital status to know more about the particu lar person. This will give way to cybercrime. For example, illegal acts, privacy invasion, and even confidential information stealing. Although technology do have their advantages, but as with many revolutionary inventions, they can radically change our lives, for better or worse. Moreover, the advancement of technology not only negatively affected our language proficiency and social life but also our health. Most teenagers and white-collar worker spend numerous hours in front of computer screen without any intense physical activity which may lead to injuries such as lumbar injuries and carpal tunnel syndrome. It is undeniable fact that computer is a vital machine for many different jobs and activities, even in learning, for adults, adolescents and children. However, the long hours of computer can contribute to increasing chance for an injury. â€Å" The more tech-time that a child engages in, the less likely it is that will get in his daily dose of physical activity†. For example, if children play too much computer games, they might experience physical and psychological problems. With higher technology, people are prone to addicted and lazy. This is because people are too dependent on the technology available today. People no longer need to leave their home for entertainment purpose and they can find the answer to anything with the web browser, Google. With the advancement of mobile phone, people do not even bother to memorise phone numbers anymore. Also, mobile phone users can download games, video and music to keep them entertained. As time pass, they forget about the people around them and addicted to the small gadgets on their hand. For instance, when we are at the restaurant, what we usually see is teenagers busy with their gadgets and even children, they no longer running around and make noise as the gadgets kept them accompanied. With excessive usage of electronic gadgets, it will weaken people’s memory and harm their eye sights. â€Å"I think modern information technology greatly simplifies our life, because a lot of what we no longer need to keep in mind, but basically there are a number of things that we will not search in the Internet every time we need it, so computer or smart phone can replace human memoryâ⠂¬ . People no longer need memorise things they need as their computer or smart phone can assists them in this matter. For example, when shopping for grocery one can simply list down on their smart phone and get it at the mall. Consequently, this will lead to a weaker memory as people rarely store information in their mind. When we look at things that are closer to our face we are likely to blink lesser than when we look at distant objects. This will cause our eyes to be drier when we spend long hours using electronic gadgets and consequently harm our eye sight. If we do not manage our usage of technology wisely, it eventually will weaken our immune system. In a nutshell, we believe the advancement of technology has negatively impacted our language proficiency, social life and health. Poor language proficiency should be countered by having more communication through internet using proper grammar and correct spellings of different words, having face-to-face communication more frequently or reading more newspaper to improve the language proficiency. We should manage our usage of technology devices by reducing the usage of smartphone for long hours, learn how to communicate and mingle with people around us and make it a habit to write in proper sentences using correct spelling and grammar. Next regarding social life, we should spend quality time with our family and friends. Moreover, try not to store most of our personal information as it might harm our safety. Lastly, regarding to health, if forced to work for long hours in front of the computer screen, we should take breaks in between to stretch our body and relax our eyes. Furthermore, society must be able to utilise technology while not allowing it to handicap social interactions, particularly for those who are easily influenced during our formative years. Our world must learn to embrace technology without allowing it to negatively impact the creation of functional adults in society. According to Greg Satell (2013), â€Å"Technology, like most human things, is a double edged sword, involving gain and loss, merit and demerit.† In conclusion, the more advanced technology becomes, the more it seems to have control over our lives.

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. 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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. 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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. Scientists and consumers have reviewed and theorized that the healthier option for the human body seems to be consuming organic f ood in comparison with traditional foods. Many people disagree about the legitimacy of the argument for organic food consumption, and whether it will result as the healthier choice.Read MoreOrganic Food - Is It Worth Its Price?1418 Words   |  6 PagesIs Organic Food Worth Its Price? Organic farming began in the late 1940’s in the United States, and in recent years it has seen a dramatic increase in popularity (Rubin 1). The sales of organic food have been increasing by about 20 percent a year over the past decade (Marcus 1). That is over ten times the rate of their conventional counterparts (Harris 1). There are 10 million consumers of organic food in the United States, yet organic food represents only one percent of the nation’s food supplyRead MoreOrganic Farming : The Effect Of The Great Depression1579 Words   |  7 Pages Essay 3 Organic farming began just as the effects of the Great Depression waned in the United States, and has seen a dramatic increase in popularity most recently (AG). The sales of organic food increased by about twenty percent a year throughout the nineteen nineties (Marcus). That is over ten times the rate of increase that conventional food experienced during the same period of time (Harris). As recently as twenty eleven, about seventy-eight percent of American families admitted to routinelyRead MoreOrganic Food Is A $29-Billion-Dollar Industry And Is Growing.1582 Words   |  7 PagesOrganic food is a $29-billion-dollar industry and is growing. Organic food is food that are manufactured, processed and handled using only organic means that meets FDA guidelines. Natural food can be labeled freely with very little to no guidelines. While conventional food still has guidelines but not as strict and being able to use chemicals and be synthesized themselves. Organic foods also have varyin g types from, Organic food which is an item that is produced using organic means, with strict standardsRead MoreAdvantages And Disadvantages Of Organic Farming1035 Words   |  5 PagesWhat is better organic farming or conventional farming? This is a question that all farmers face. Each type of farming as its own benefits and disadvantages. Organic farming and conventional farming are different in many different ways. I know farmers from both sides. I know farmers who practice organic farming and I also know farmers who practice conventional farming as well as some farmers who use a combination of the two types of farming. But I have never really know all of the differences betweenRead MoreWhat Are The Pros And Cons Of Organic Foods1393 Words   |  6 Pages Organic Foods Courtney Rathmann HLTH 232 10/1/2017 Hearing the term organic foods, we think what are those and how do they compare to conventional foods? Organic foods and other ingredients are grown without the use of pesticides, synthetic fertilizers, sewage sludge, genetically modified organisms, or ionizing radiation. And animals that produce organic meat, poultry, eggs and dairy products do not take antibiotics or growth hormones. Conventional foods are the total oppositeRead MoreHow Organic Food Is Healthier For You1524 Words   |  7 PagesOrganic food consists of any crops or animal product produced without the use of pesticides, man-made fertilizers, additives, or growth regulators. ‘In 2002 the USDA created national organic standards, overriding any state regulators and creating a labeling system.’ (Griswold 2015) The Labels include different levels such as â€Å"100 percent organic† which means the product must be made from only organic products, â€Å"organic† products that have at least 95 pe rcent organic ingredients, and products, â€Å"containingRead MoreThe Use Of Pesticides And Growth Hormone1530 Words   |  7 Pagesworld’s population continuing to increase, the demand for food is higher than ever. A growing population means more demand on food. â€Å"The world population will rise to 9.3 billion in 2050 and surpass 10 billion by the end of this century.† (Sanyal) This should say something about our growing population that is still continuing to grow to this day. This increase in food demand also calls for more efficient ways of growing and providing food without causing any damage to our environment or our healthRead MoreOrganic vs. Conventional Food1235 Words   |  5 PagesOrganic vs. Conventional Food In the United States consumers are inundated with every option imaginable for food. Among those options is the choice of organic or conventional food. Health experts will tout the virtues of organic food as being better for the consumer and preventing many diseases, however, there seems to be more to it than that. When speaking with friends, especially those living on a budget, the philosophy leans more towards the difference between fresh and processed food, andRead MoreOrganic vs. Conventional Foods Essay1119 Words   |  5 Pagesdemand for food is higher than ever. This increase in food demand also calls for more efficient ways of growing and providing the food. Two methods that are very controversial are the organic and conventional method. While many people support the organic method because of its known benefits, others feel that it is an over inflated industry that cheats consumers out of their money. But recently many studies have disproved those critics. These studies prove that Organic food is a better choice than