Event Extraction from Unstructured Text Data
We extend a bootstrapping method that was initially developed for extracting relations from webpages to the problem of extracting content from large collections of short unstructured text. Such data appear as field notes in enterprise applications and as messages in social media services. The method iteratively learns sentence patterns that match a set of representative event mentions and then extracts different mentions using the learnt patterns. At every step, the semantic similarity between the text and set of patterns is used to determine if the pattern was matched. Semantic similarity is calculated using the WordNet lexical database. Local structure features such as bigrams are extracted where possible from the data to improve the accuracy of pattern matching. We rank and filter the learnt patterns to balance the precision and recall of the approach with respect to extracted events. We demonstrate this approach on two different datasets. One is a collection of field notes from an enterprise dataset. The other is a collection of “tweets” collected from the Twitter social network. We evaluate the accuracy of the extracted events when method parameters are varied.
KeywordsSemantic Similarity Content Extraction Stop Word Event Extraction Relation Extraction
This work is supported by Chevron U.S.A. Inc. under the joint project, Center for Interactive Smart Oilfield Technologies (CiSoft), at the University of Southern California.
- 1.Agichtein, E., Gravano, L.: Snowball: extracting relations from large plain-text collections. In: ACM Conference on Digital Libraries, pp. 85–94 (2000)Google Scholar
- 2.Allan, J., Papka, R., Lavrenko, V.: On-line new event detection and tracking. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 37–45. ACM (1998)Google Scholar
- 3.Banko, M., Cafarella, M.J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction for the web. In: IJCAI, vol. 7, pp. 2670–2676 (2007)Google Scholar
- 4.Benson, E., Haghighi, A., Barzilay, R.: Event discovery in social media feeds. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 389–398 (2011)Google Scholar
- 5.Bouma, G.: Normalized (pointwise) mutual information in collocation extraction. In: Proceedings of the Biennial GSCL Conference, pp. 31–40 (2009)Google Scholar
- 8.Doddington, G.R., Mitchell, A., Przybocki, M.A., Ramshaw, L.A., Strassel, S., Weischedel, R.M.: The automatic content extraction (ace) program-tasks, data, and evaluation. In: LREC (2004)Google Scholar
- 10.Pedersen, T., Patwardhan, S., Michelizzi, J.: Wordnet:: Similarity: measuring the relatedness of concepts. In: Demonstration Papers at HLT-NAACL 2004, pp. 38–41. Association for Computational Linguistics (2004)Google Scholar
- 11.Ritter, A., Mausam, E.O., Clark, S.: Open domain event extraction from twitter. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1104–1112. ACM (2012)Google Scholar
- 12.Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, pp. 851–860. ACM (2010)Google Scholar
- 13.Zhao, W.X., Jiang, J., He, J., Song, Y., Achananuparp, P., Lim, E.-P., Li, X.: Topical keyphrase extraction from Twitter. In: ACL: Human Language Technologies, pp. 379–388 (2011)Google Scholar
- 14.Zong, B., Wu, Y., Song, J., Singh, A.K., Cam, H., Han, J., Yan, X.: Towards scalable critical alert mining. In: 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1057–1066 (2014)Google Scholar