2006 IntegProbExtrModelsAndDMtoDiscoverRelations

• (Culotta et al., 2006) ⇒ Aron Culotta, Andrew McCallum, Jonathan Betz. (2006). “Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text.” In: Proceedings of HLT-NAACL Conference (HLT-NAACL 2006). doi:10.3115/1220835.1220873

Subject Headings: Relation Mention Recognition Algorithm, Conditional Random Fields, Relational Pattern, Joint Inference Algorithm.

Cited By

• Google Scholar: ~ 193 Citations Retrieved: 2019-09-26.
• Semantic Scholar: ~ 130 Citations Retrieved: 2019-09-26.

Abstract

In order for relation extraction systems to obtain human-level performance, they must be able to incorporate relational patterns inherent in the data (for example, that one's sister is likely one's mother's daughter, or that children are likely to attend the same college as their parents). Hand-coding such knowledge can be time-consuming and inadequate. Additionally, there may exist many interesting, unknown relational patterns that both improve extraction performance and provide insight into text. We describe a probabilistic extraction model that provides mutual benefits to both "top-down" relational pattern discovery and "bottom-up" relation extraction.

References

• (Agichtein and Gravano, 2000) ⇒ Eugene Agichtein and L. Gravano. (2000). “Snowball: Extracting Relations from Large Plain-Text Collections.” In: Proceedings of the 5th ACM International Conference on Digital Libraries (DL-2000).
• (Brin, 1998) ⇒ Sergey Brin. (1998). “Extracting Patterns and Relations from the World Wide Web.” In: Proceedings of the EDBT 1998 Workshop on the Web and Databases (WebDB 1998).
• (Bunescu & Mooney, 2005) ⇒ Razvan C. Bunescu, and Raymond Mooney. (2006). “Subsequence Kernels for Relation Extraction.” In: Proceedings of the 19th Conference on Neural Information Processing Systems (NIPS 2005).
• Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew K. Mc-Callum, Tom M. Mitchell, Kamal Nigam, and Se´an Slattery. (1998). Learning to extract symbolic knowledge from the World Wide Web. In: Proceedings of AAAI-98, 15th Conference of the American Association for Artificial Intelligence, pages 509–516, Madison, US. AAAI Press, Menlo Park, US.
• Aron Culotta and Andrew McCallum. (2004). Confidence estimation for information extraction. In Human Language Technology Conference (HLT 2004), Boston, MA.
• (Culotta & Sorensen, 2004) ⇒ Aron Culotta, and Jeffrey S. Sorensen. (2004). “Dependency Tree Kernels for Relation Extraction.” In: Proceedings of ACL Conference (ACL 2004).
• L. Dehaspe. (1997). Maximum entropy modeling with clausal constraints. In: Proceedings of the Seventh International Workshop on Inductive Logic Programming, pages 109–125, Prague, Czech Republic.
• Marti Hearst. (1999). Untangling text data mining. In 37th Annual Meeting of the Association for Computational Linguistics.
• (Kambhatla, 2004) ⇒ Nanda Kambhatla. (2004). “Combining Lexical, Syntactic, and Semantic Features with Maximum Entropy Models for Extracting Relations.” In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004). doi:10.3115/1219044.1219066
• John D. Lafferty, Andrew McCallum, and Fernando Pereira. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of 18th International Conference on Machine Learning, pages 282–289. Morgan Kaufmann, San Francisco, CA.
• Gideon Mann and David Yarowsky. (2005). Multi-field information extraction and cross-document fusion. In ACL. D. Masterson and N. Kushmerik. (2003). Information extraction from multi-document threads. In ECML-2003: Workshop on Adaptive Text Extraction and Mining, pages 34–41.
• (McCallum and Jensen, 2003) ⇒ Andrew McCallum, and David Jensen. (2003). “A Note on the Unification of Information Extraction and Data Mining using Conditional-Probability, Relational Models.” In: Proceedings of the IJCAI03 Workshop on Learning Statistical Models from Relational Data.
• Andrew McCallum. (2002). Mallet: A machine learning for language toolkit. http://mallet.cs.umass.edu.
• Andrew McCallum. (2003). Efficiently inducing features of conditional random fields. In Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI03).
• (Miller et al., 2000) ⇒ Scott Miller, Heidi Fox, Lance Ramshaw, and Ralph Weischedel. (2000). “A Novel Use of Statistical Parsing to Extract Information from Text.” In: Proceedings of the 1st North American Chapter of the Association for Computational Linguistics Conference (NAACL 2000).
• Raymond Mooney, and Razvan C. Bunescu. (2005). Mining knowledge from text using information extraction. SigKDD Explorations on Text Mining and Natural Language Processing.
• Un Yong Nahm and Raymond Mooney. (2000). A mutually beneficial integration of data mining and information extraction. In AAAI/IAAI.
• Bradley L. Richards and Raymond Mooney. (1992). Learning relations by pathfinding. In: Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), pages 50–55, San Jose, CA.
• Dan Roth and Wen tau Yih. (2002). Probabilistic reasoning for entity and relation recognition. In COLING. Sunita Sarawagi and William W. Cohen. (2004). Semi-markov conditional random fields for information extraction. In NIPS 04.
• Charles Sutton and Andrew McCallum. (2004). Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data. In: Proceedings of the Twenty-First International Conference on Machine Learning (ICML).
• Charles Sutton and Andrew McCallum. (2006). An introduction to conditional random fields for relational learning. In Lise Getoor and Ben Taskar, editors, Introduction to Statistical Relational Learning. MIT Press. To appear.
• Dmitry Zelenko, Chinatsu Aone, and Anthony Richardella. (2003). Kernel methods for relation extraction. Journal of Machine Learning Research, 3:1083–1106.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2006 IntegProbExtrModelsAndDMtoDiscoverRelations	Aron Culotta Jonathan Betz Andrew McCallum			Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text		Proceedings of HLT-NAACL Conference	http://www.cs.umass.edu/~culotta/pubs/culotta06integrating.pdf	10.3115/1220835.1220873		2006