The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Open domain event extraction from twitter
Abstract
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.
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Cites
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Self-citationNamed Entity Recognition in Tweets: An Experimental Study
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Cited by
A survey on sentiment analysis methods, applications, and challenges
Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10144-1 · Cited by 1,528
Processing Social Media Messages in Mass Emergency: A Survey
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Social media analytics – Challenges in topic discovery, data collection, and data preparation
Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2017.12.002 · Cited by 803
SemEval-2015 Task 10: Sentiment Analysis in Twitter
Rosenthal, S., Nakov, P., Kiritchenko, S., Mohammad, S., Ritter, A., & Stoyanov, V. (2015) · Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015) · doi:10.18653/v1/s15-2078 · Cited by 367
Self-citationYu, J., Jiang, J., Yang, L., & Xia, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.306 · Cited by 281
Metadata
- Authors
- Ritter, A., Mausam, Etzioni, O., & Clark, S.
- Venue
- Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2012-08-12
- Pages
- 1104-1112
- OpenAlex
- W2123661878
- Cited by
- 592
- References
- 47
- FWCI
- 41.11
- Type
- conference paper
- Language
- en
- Open access
- closed
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