One more trained method
", "tags": [], "relations": {}, "dateAdded": "2015-02-25T00:14:48Z", "dateModified": "2015-02-25T00:14:48Z" } }, { "key": "QCAB563N", "version": 8, "library": { "type": "group", "id": 270765, "name": "Social Media Event Detection", "links": { "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/270765/items/QCAB563N", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection/items/QCAB563N", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/270765/items/59CFNRKV", "type": "application/json" } }, "meta": { "createdByUser": { "id": 20034, "username": "dshepard", "name": "Dave Shepard", "links": { "alternate": { "href": "https://www.zotero.org/dshepard", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "QCAB563N", "version": 8, "parentItem": "59CFNRKV", "itemType": "note", "note": "They adapt an existing POS tagger.
", "tags": [], "relations": {}, "dateAdded": "2015-02-25T00:14:48Z", "dateModified": "2015-02-25T00:14:48Z" } }, { "key": "4993BANN", "version": 8, "library": { "type": "group", "id": 270765, "name": "Social Media Event Detection", "links": { "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/270765/items/4993BANN", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection/items/4993BANN", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/270765/items/HVJ8AWR2", "type": "application/json" } }, "meta": { "createdByUser": { "id": 20034, "username": "dshepard", "name": "Dave Shepard", "links": { "alternate": { "href": "https://www.zotero.org/dshepard", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "4993BANN", "version": 8, "parentItem": "HVJ8AWR2", "itemType": "note", "note": "POSLDA method: keeps track of part of speech as well as actual words
", "tags": [], "relations": {}, "dateAdded": "2015-02-25T00:14:48Z", "dateModified": "2015-02-25T00:14:48Z" } }, { "key": "TMZS72MW", "version": 8, "library": { "type": "group", "id": 270765, "name": "Social Media Event Detection", "links": { "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/270765/items/TMZS72MW", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection/items/TMZS72MW", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/270765/items/GCKTN2I7", "type": "application/json" } }, "meta": { "createdByUser": { "id": 20034, "username": "dshepard", "name": "Dave Shepard", "links": { "alternate": { "href": "https://www.zotero.org/dshepard", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "TMZS72MW", "version": 8, "parentItem": "GCKTN2I7", "itemType": "note", "note": "K-Nearest-Neighbors approach
\nChallenges for tweets:
\nNER also involves Named-Entity Normalization (NEN).
\nA particular challenge in tweets because errors can propagate.
\n\n
Graphical model for NEN/NER
", "tags": [], "relations": {}, "dateAdded": "2015-02-25T00:14:48Z", "dateModified": "2015-02-25T00:14:48Z" } }, { "key": "HAK2QXJ5", "version": 8, "library": { "type": "group", "id": 270765, "name": "Social Media Event Detection", "links": { "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/270765/items/HAK2QXJ5", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection/items/HAK2QXJ5", "type": "text/html" } }, "meta": { "createdByUser": { "id": 20034, "username": "dshepard", "name": "Dave Shepard", "links": { "alternate": { "href": "https://www.zotero.org/dshepard", "type": "text/html" } } }, "creatorSummary": "Qin", "parsedDate": "2015-01-07", "numChildren": 0 }, "data": { "key": "HAK2QXJ5", "version": 8, "itemType": "journalArticle", "title": "Hero on Twitter, Traitor on News How Social Media and Legacy News Frame Snowden", "creators": [ { "creatorType": "author", "firstName": "Jie", "lastName": "Qin" } ], "abstractNote": "Is Edward Snowden a hero or traitor? In what ways do frames on social media and legacy news differ in covering the incident of Edward Snowden? Utilizing the approach of semantic network analysis, the study found social media users associated Snowden’s case with other whistle-blowers, bipartisan issues, and personal privacy issues, while professional journalists associated the Snowden incident with issues of national security and international relations. Frames on social media portray Edward Snowden as a hero while the frames on legacy news make him a traitor. The study further identified media frames on social media and legacy news differ in two ways: word selection and word salience. In addition, the study discussed the challenges and opportunities of framing analysis in the context of social media.", "publicationTitle": "The International Journal of Press/Politics", "publisher": "", "place": "", "date": "2015-01-07", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "1940161214566709", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "The International Journal of Press/Politics", "DOI": "10.1177/1940161214566709", "citationKey": "", "url": "http://hij.sagepub.com/content/early/2015/01/06/1940161214566709", "accessDate": "2015-02-23T18:49:36Z", "PMID": "", "PMCID": "", "ISSN": "1940-1612, 1940-1620", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "hij.sagepub.com", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "framing theory", "type": 1 }, { "tag": "legacy news", "type": 1 }, { "tag": "public opinion", "type": 1 }, { "tag": "semantic network", "type": 1 }, { "tag": "social media", "type": 1 } ], "collections": [ "FF33UNMF" ], "relations": {}, "dateAdded": "2015-02-25T00:14:48Z", "dateModified": "2015-02-25T00:14:48Z" } }, { "key": "BJZE5C4M", "version": 8, "library": { "type": "group", "id": 270765, "name": "Social Media Event Detection", "links": { "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/270765/items/BJZE5C4M", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection/items/BJZE5C4M", "type": "text/html" } }, "meta": { "createdByUser": { "id": 20034, "username": "dshepard", "name": "Dave Shepard", "links": { "alternate": { "href": "https://www.zotero.org/dshepard", "type": "text/html" } } }, "creatorSummary": "HIZUKA et al.", "parsedDate": "2007", "numChildren": 0 }, "data": { "key": "BJZE5C4M", "version": 8, "itemType": "journalArticle", "title": "Semantic Role Labeling based on Japanese FrameNet", "creators": [ { "creatorType": "author", "firstName": "SHINSUKE", "lastName": "HIZUKA" }, { "creatorType": "author", "firstName": "HIROYUKI", "lastName": "OKAMOTO" }, { "creatorType": "author", "firstName": "HIROAKI", "lastName": "SAITO" }, { "creatorType": "author", "firstName": "KYOKOHIROSE", "lastName": "OHARA" } ], "abstractNote": "This paper proposes a stochastic model for semantic role labeling based on Japanese FrameNet and suggests a method to acquire it by machine learning. The model distinguishes semantic roles which cannot be separated by surface cases. The model receives a sentence and its predicate, identifies its predicate argument structure, then identifies the arguments to be labeled, and finally labels them with adequate semantic roles. The system based on the model achieved 77% precision and 68% recall in identifying the semantic roles of the pre-segmented arguments under the condition that the system labels the role whose certainty is more than the threshold. For more difficult tasks of identifying the arguments which should be labeled and their roles, the system attained 63% precision and 43% recall under the same condition. The system also achieved to label different semantic roles to the arguments whose surface cases are identical.", "publicationTitle": "Journal of Natural Language Processing", "publisher": "", "place": "", "date": "2007", "volume": "14", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "43-66", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.5715/jnlp.14.43", "citationKey": "", "url": "http://ci.nii.ac.jp/naid/130004291900/", "accessDate": "2015-02-23T18:35:19Z", "PMID": "", "PMCID": "", "ISSN": "1340-7619", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "CiNii", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Maximum entropy method", "type": 1 }, { "tag": "Semantic role labeling", "type": 1 }, { "tag": "Support vector machine", "type": 1 } ], "collections": [ "FF33UNMF" ], "relations": {}, "dateAdded": "2015-02-25T00:14:48Z", "dateModified": "2015-02-25T00:14:48Z" } }, { "key": "QJ5DSMRU", "version": 8, "library": { "type": "group", "id": 270765, "name": "Social Media Event Detection", "links": { "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/270765/items/QJ5DSMRU", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection/items/QJ5DSMRU", "type": "text/html" } }, "meta": { "createdByUser": { "id": 20034, "username": "dshepard", "name": "Dave Shepard", "links": { "alternate": { "href": "https://www.zotero.org/dshepard", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "QJ5DSMRU", "version": 8, "itemType": "note", "note": "", "tags": [], "collections": [ "FF33UNMF" ], "relations": {}, "dateAdded": "2015-02-25T00:14:48Z", "dateModified": "2015-02-25T00:14:48Z" } }, { "key": "DVQ4RCJ9", "version": 8, "library": { "type": "group", "id": 270765, "name": "Social Media Event Detection", "links": { "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/270765/items/DVQ4RCJ9", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection/items/DVQ4RCJ9", "type": "text/html" } }, "meta": { "createdByUser": { "id": 20034, "username": "dshepard", "name": "Dave Shepard", "links": { "alternate": { "href": "https://www.zotero.org/dshepard", "type": "text/html" } } }, "creatorSummary": "Kase", "parsedDate": "2013-06", "numChildren": 0 }, "data": { "key": "DVQ4RCJ9", "version": 8, "itemType": "report", "title": "Accelerating Exploitation of Low-grade Intelligence through Semantic Text Processing of Social Media", "creators": [ { "creatorType": "author", "firstName": "Sue E.", "lastName": "Kase" } ], "abstractNote": "Social media will play a key role in many areas of intelligence operations with the development of knowledge extraction analytics. The data contained within these social networking services present many challenges, but the value obtained from detecting subtle and hidden information exchanges and environmental observations are vast. The analytics that will drive social media knowledge exploitation include advanced text processing technologies allowing multi-faceted concept or frame-based queries within a familiar search engine interface for users who are not expert analysts or who may be operating with limited intelligence resources in disadvantaged information collection situations. In this paper, we demonstrate constructing frame-based semantic search queries using models of concepts and roles extracted from social media content. This approach to searching allows intuitive query formulations requiring less up-front knowledge of the search parameters. We demonstrate this semantic search capability by querying over seven million tweets captured during the Arab Spring uprisings.", "reportNumber": "", "reportType": "", "institution": "", "place": "", "date": "2013-06", "seriesTitle": "", "seriesNumber": "", "pages": "", "DOI": "", "ISBN": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "Defense Technical Information Center", "callNumber": "", "rights": "Approved for public release; distribution is unlimited.", "extra": "", "tags": [ { "tag": "*EGYPT UPRISINGS", "type": 1 }, { "tag": "*INFORMATION RETRIEVAL", "type": 1 }, { "tag": "*INTELLIGENCE", "type": 1 }, { "tag": "*INTELLIGENCE EXPLOITATION", "type": 1 }, { "tag": "*INTERNET", "type": 1 }, { "tag": "*ONLINE COMMUNITIES", "type": 1 }, { "tag": "*POLITICAL REVOLUTION", "type": 1 }, { "tag": "*SEMANTIC TEXT PROCESSING", "type": 1 }, { "tag": "*SEMANTICS", "type": 1 }, { "tag": "*SOCIAL MEDIA", "type": 1 }, { "tag": "*TEXT PROCESSING", "type": 1 }, { "tag": "ADVANCED ANALYTICS", "type": 1 }, { "tag": "ARAB SPRING TWEETS", "type": 1 }, { "tag": "ARAB SPRING UPRISINGS", "type": 1 }, { "tag": "BRIEFING CHARTS", "type": 1 }, { "tag": "CONTOUR", "type": 1 }, { "tag": "Computer Systems", "type": 1 }, { "tag": "EGYPT", "type": 1 }, { "tag": "EXTRACTION", "type": 1 }, { "tag": "FRAME-BASED SEMANTIC SEARCH QUERIES", "type": 1 }, { "tag": "FRAMENET-BASED SEMANTIC SEARCHING", "type": 1 }, { "tag": "GOVERNMENT(FOREIGN)", "type": 1 }, { "tag": "Government and Political Science", "type": 1 }, { "tag": "INTERACTIONS", "type": 1 }, { "tag": "INTERPERSONAL RELATIONS", "type": 1 }, { "tag": "Information Science", "type": 1 }, { "tag": "KNOWLEDGE EXPLOITATION", "type": 1 }, { "tag": "LOW-GRADE INTELLIGENCE", "type": 1 }, { "tag": "Linguistics", "type": 1 }, { "tag": "Psychology", "type": 1 }, { "tag": "ROLES(BEHAVIOR)", "type": 1 }, { "tag": "SOCIAL NETWORKS", "type": 1 }, { "tag": "SYMPOSIA", "type": 1 }, { "tag": "TAHRIR SQUARE(EGYPT)", "type": 1 }, { "tag": "Twitter", "type": 1 }, { "tag": "WORDS(LANGUAGE)", "type": 1 }, { "tag": "information extraction", "type": 1 }, { "tag": "situational awareness", "type": 1 } ], "collections": [ "FF33UNMF" ], "relations": {}, "dateAdded": "2015-02-25T00:14:48Z", "dateModified": "2015-02-25T00:14:48Z" } }, { "key": "BCJ8SVG8", "version": 8, "library": { "type": "group", "id": 270765, "name": "Social Media Event Detection", "links": { "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/270765/items/BCJ8SVG8", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection/items/BCJ8SVG8", "type": "text/html" } }, "meta": { "createdByUser": { "id": 20034, "username": "dshepard", "name": "Dave Shepard", "links": { "alternate": { "href": "https://www.zotero.org/dshepard", "type": "text/html" } } }, "creatorSummary": "Ritter et al.", "parsedDate": "2012", "numChildren": 0 }, "data": { "key": "BCJ8SVG8", "version": 8, "itemType": "conferencePaper", "title": "Open Domain Event Extraction from Twitter", "creators": [ { "creatorType": "author", "firstName": "Alan", "lastName": "Ritter" }, { "creatorType": "author", "name": "Mausam" }, { "creatorType": "author", "firstName": "Oren", "lastName": "Etzioni" }, { "creatorType": "author", "firstName": "Sam", "lastName": "Clark" } ], "abstractNote": "Tweets are the most up-to-date and inclusive stream of in- formation 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 first open-domain event-extraction and categorization system for Twitter. We demonstrate that accurately extracting an open-domain calendar of significant 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.", "proceedingsTitle": "Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining", "conferenceName": "", "publisher": "ACM", "place": "New York, NY, USA", "date": "2012", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "1104–1112", "series": "KDD '12", "seriesNumber": "", "DOI": "10.1145/2339530.2339704", "ISBN": "978-1-4503-1462-6", "citationKey": "", "url": "http://doi.acm.org/10.1145/2339530.2339704", "accessDate": "2015-02-23T18:04:54Z", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ACM Digital Library", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "information extraction", "type": 1 }, { "tag": "social media", "type": 1 } ], "collections": [ "GHXFZW3X" ], "relations": {}, "dateAdded": "2015-02-25T00:14:48Z", "dateModified": "2015-02-25T00:14:48Z" } }, { "key": "ARF8H5NF", "version": 8, "library": { "type": "group", "id": 270765, "name": "Social Media Event Detection", "links": { "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/270765/items/ARF8H5NF", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection/items/ARF8H5NF", "type": "text/html" } }, "meta": { "createdByUser": { "id": 20034, "username": "dshepard", "name": "Dave Shepard", "links": { "alternate": { "href": "https://www.zotero.org/dshepard", "type": "text/html" } } }, "creatorSummary": "Derczynski et al.", "parsedDate": "2013", "numChildren": 0 }, "data": { "key": "ARF8H5NF", "version": 8, "itemType": "conferencePaper", "title": "Twitter Part-of-Speech Tagging for All: Overcoming Sparse and Noisy Data.", "creators": [ { "creatorType": "author", "firstName": "Leon", "lastName": "Derczynski" }, { "creatorType": "author", "firstName": "Alan", "lastName": "Ritter" }, { "creatorType": "author", "firstName": "Sam", "lastName": "Clark" }, { "creatorType": "author", "firstName": "Kalina", "lastName": "Bontcheva" } ], "abstractNote": "", "proceedingsTitle": "RANLP", "conferenceName": "", "publisher": "", "place": "", "date": "2013", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "198–206", "series": "", "seriesNumber": "", "DOI": "", "ISBN": "", "citationKey": "", "url": "http://anthology.aclweb.org/R/R13/R13-1.pdf#page=226", "accessDate": "2015-02-20T23:35:23Z", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "Twitter Part-of-Speech Tagging for All", "language": "", "libraryCatalog": "Google Scholar", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "UB65FR5S", "VPECQQ5Q" ], "relations": {}, "dateAdded": "2015-02-25T00:14:48Z", "dateModified": "2015-02-25T00:14:48Z" } }, { "key": "DHEZBJ2I", "version": 8, "library": { "type": "group", "id": 270765, "name": "Social Media Event Detection", "links": { "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/270765/items/DHEZBJ2I", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection/items/DHEZBJ2I", "type": "text/html" } }, "meta": { "createdByUser": { "id": 20034, "username": "dshepard", "name": "Dave Shepard", "links": { "alternate": { "href": "https://www.zotero.org/dshepard", "type": "text/html" } } }, "creatorSummary": "Pak and Paroubek", "parsedDate": "2010", "numChildren": 0 }, "data": { "key": "DHEZBJ2I", "version": 8, "itemType": "journalArticle", "title": "Twitter as a Corpus for Sentiment Analysis and Opinion Mining.", "creators": [ { "creatorType": "author", "firstName": "Alexander", "lastName": "Pak" }, { "creatorType": "author", "firstName": "Patrick", "lastName": "Paroubek" } ], "abstractNote": "", "publicationTitle": "LREC", "publisher": "", "place": "", "date": "2010", "volume": "10", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "1320–1326", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "http://incc-tps.googlecode.com/svn/trunk/TPFinal/bibliografia/Pak%20and%20Paroubek%20(2010).%20Twitter%20as%20a%20Corpus%20for%20Sentiment%20Analysis%20and%20Opinion%20Mining.pdf", "accessDate": "2015-02-20T23:33:07Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Google Scholar", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "UB65FR5S", "VPECQQ5Q" ], "relations": {}, "dateAdded": "2015-02-25T00:14:48Z", "dateModified": "2015-02-25T00:14:48Z" } }, { "key": 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association for computational linguistics", "conferenceName": "", "publisher": "Association for Computational Linguistics", "place": "", "date": "2010", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "689–692", "series": "", "seriesNumber": "", "DOI": "", "ISBN": "", "citationKey": "", "url": "http://dl.acm.org/citation.cfm?id=1858100", "accessDate": "2015-02-20T23:27:14Z", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Google Scholar", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "UB65FR5S", "VPECQQ5Q" ], "relations": {}, "dateAdded": "2015-02-25T00:14:48Z", "dateModified": "2015-02-25T00:14:48Z" } }, { "key": "JH6F27H7", "version": 8, "library": { "type": "group", "id": 270765, "name": "Social Media Event Detection", "links": { "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection", "type": "text/html" } } }, "links": { "self": { "href": 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We use a version of Malt which is trained on gold standard phrase structure Wall Street Journal (WSJ) trees converted to Stanford labelled dependencies. We observe a drastic drop in performance moving from our in-domain WSJ test set to the new Twitter dataset, much of which has to do with the propagation of part-of-speech tagging er- rors. Retraining Malt on dependency trees produced by a state-of-the-art phrase structure parser, which has itself been self-trained on web material, results in a sig- nificant improvement. We analyse this improvement by examining in detail the effect of the retraining on indi- vidual dependency types.", "proceedingsTitle": "AAAI 2011 Workshop On Analyzing Microtext", "conferenceName": "", "publisher": "", "place": "United States", "date": "2011", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "20-25", "series": "", "seriesNumber": "", "DOI": "", "ISBN": "", "citationKey": "", "url": "https://hal.archives-ouvertes.fr/hal-00702445", "accessDate": "2015-02-20T23:21:58Z", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "#hardtoparse", "language": "", "libraryCatalog": "HAL Archives Ouvertes", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "UB65FR5S", "VPECQQ5Q" ], "relations": {}, "dateAdded": "2015-02-25T00:14:48Z", "dateModified": "2015-02-25T00:14:48Z" } }, { "key": "4G8HJVK5", "version": 8, "library": { "type": "group", "id": 270765, "name": "Social Media Event Detection", "links": { "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/270765/items/4G8HJVK5", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection/items/4G8HJVK5", "type": "text/html" } }, "meta": { "createdByUser": { "id": 20034, "username": "dshepard", "name": "Dave Shepard", "links": { "alternate": { "href": "https://www.zotero.org/dshepard", "type": "text/html" } } }, "creatorSummary": "Jiang et al.", "parsedDate": "2011", "numChildren": 0 }, "data": { "key": "4G8HJVK5", "version": 8, "itemType": "conferencePaper", "title": "Target-dependent Twitter Sentiment Classification", "creators": [ { "creatorType": "author", "firstName": "Long", "lastName": "Jiang" }, { "creatorType": "author", "firstName": "Mo", "lastName": "Yu" }, { "creatorType": "author", "firstName": "Ming", "lastName": "Zhou" }, { "creatorType": "author", "firstName": "Xiaohua", "lastName": "Liu" }, { "creatorType": "author", "firstName": "Tiejun", "lastName": "Zhao" } ], "abstractNote": "Sentiment analysis on Twitter data has attracted much attention recently. In this paper, we focus on target-dependent Twitter sentiment classification; namely, given a query, we classify the sentiments of the tweets as positive, negative or neutral according to whether they contain positive, negative or neutral sentiments about that query. Here the query serves as the target of the sentiments. The state-of-the-art approaches for solving this problem always adopt the target-independent strategy, which may assign irrelevant sentiments to the given target. Moreover, the state-of-the-art approaches only take the tweet to be classified into consideration when classifying the sentiment; they ignore its context (i.e., related tweets). However, because tweets are usually short and more ambiguous, sometimes it is not enough to consider only the current tweet for sentiment classification. In this paper, we propose to improve target-dependent Twitter sentiment classification by 1) incorporating target-dependent features; and 2) taking related tweets into consideration. According to the experimental results, our approach greatly improves the performance of target-dependent sentiment classification.", "proceedingsTitle": "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1", "conferenceName": "", "publisher": "Association for Computational Linguistics", "place": "Stroudsburg, PA, USA", "date": "2011", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "151–160", "series": "HLT '11", "seriesNumber": "", "DOI": "", "ISBN": "978-1-932432-87-9", "citationKey": "", "url": "http://dl.acm.org/citation.cfm?id=2002472.2002492", "accessDate": "2015-02-20T23:05:50Z", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ACM Digital Library", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "UB65FR5S", "VPECQQ5Q" ], "relations": {}, "dateAdded": "2015-02-25T00:14:48Z", "dateModified": "2015-02-25T00:14:48Z" } }, { "key": "35P666GE", "version": 8, "library": { "type": "group", "id": 270765, "name": "Social Media Event Detection", "links": { "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/270765/items/35P666GE", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection/items/35P666GE", "type": "text/html" } }, "meta": { "createdByUser": { "id": 20034, "username": "dshepard", "name": "Dave Shepard", "links": { "alternate": { "href": "https://www.zotero.org/dshepard", "type": "text/html" } } }, "creatorSummary": "Locke", "parsedDate": "2009", "numChildren": 0 }, "data": { "key": "35P666GE", "version": 8, "itemType": "journalArticle", "title": "Named entity recognition: Adapting to microblogging", "creators": [ { "creatorType": "author", "firstName": "Brian William", "lastName": "Locke" } ], "abstractNote": "", "publicationTitle": "", "publisher": "", "place": "", "date": "2009", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "http://scholar.colorado.edu/cgi/viewcontent.cgi?article=1028&context=csci_ugrad", "accessDate": "2015-02-23T18:24:49Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "Named entity recognition", "language": "", "libraryCatalog": "Google Scholar", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "K3HNNZFA" ], "relations": {}, "dateAdded": "2015-02-25T00:14:48Z", "dateModified": "2015-02-25T00:14:48Z" } }, { "key": "SUB3AQKP", "version": 8, "library": { "type": "group", "id": 270765, "name": "Social Media Event Detection", "links": { "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/270765/items/SUB3AQKP", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/social_media_event_detection/items/SUB3AQKP", "type": "text/html" } }, "meta": { "createdByUser": { "id": 20034, "username": "dshepard", "name": "Dave Shepard", "links": { "alternate": { "href": "https://www.zotero.org/dshepard", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "SUB3AQKP", "version": 8, "itemType": "note", "note": "
Summary: NER on Tweets
\nGeneral-purpose tools don't perform well (Ritter, Liu)
\nChallenges of NER on tweets:
\nSome approaches:
\n