2007 ASeedDrivBotUpMLFrameworkForExtrRels
- (Xu et al., 2007) ⇒ F. Xu, H. Uszkoreit, H. Li. (2007). “A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity.” In: Proceedings of ACL 2007 (ACL 2007).
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Abstract
A minimally supervised machine learning framework is described for extracting relations of various complexity. Bootstrapping starts from a small set of n-ary relation instances as “seeds”, in order to automatically learn pattern rules from parsed data, which then can extract new instances of the relation and its projections. We propose a novel rule representation enabling the composition of n-ary relation rules on top of the rules for projections of the relation. The compositional approach to rule construction is supported by a bottom-up pattern extraction method. In comparison to other automatic approaches, our rules cannot only localize relation arguments but also assign their exact target argument roles. The method is evaluated in two tasks: the extraction of Nobel Prize awards and management succession events. Performance for the new Nobel Prize task is strong. For the management succession task the results compare favorably with those of existing pattern acquisition approaches.
References
- Eugene Agichtein and L. Gravano. (2000). Snowball: extracting relations from large plain-text collections. In ACM 2000, pages 85–94, Texas, USA.
- S. Brin. Extracting patterns and relations from the World-Wide Web. In: Proceedings of 1998 Int'l Workshop on the Web and Databases (WebDB '98), March 1998.
- M. E. Califf and Raymond Mooney. (2004). Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction. Journal of Machine Learning Research, MIT Press.
- W. Drozdzynski, H.-U.Krieger, J. Piskorski; U. Schäfer, and F. Xu. (2004). Shallow Processing with Unification and Typed Feature Structures — Foundations and Applications. Künstliche Intelligenz 1:17 — 23.
- M. A. Greenwood and M. Stevenson. (2006). Improving Semi-supervised Acquisition of Relation Extraction Patterns. In: Proceedings of the Workshop on Information Extraction Beyond the Document, Australia.
- Dekang Lin. (1998). Dependency-based evaluation of MINIPAR. In Workshop on the Evaluation of Parsing Systems, Granada, Spain.
- MUC. (1995). Proceedings of the Sixth Message Understanding Conference (MUC-6), Morgan Kaufmann.
- Ellen Riloff. (1996). Automatically Generating Extraction Patterns from Untagged Text. In: Proceedings of the Thirteenth National Conference on Articial Intelligence, pages 1044–1049, Portland, OR, August.
- M. Stevenson and Mark A. Greenwood. (2006). Comparing Information Extraction Pattern Models. In: Proceedings of the Workshop on Information Extraction Beyond the Document, Sydney, Australia.
- K. Sudo, Satoshi Sekine, and Ralph Grishman. (2003). An Improved Extraction Pattern Representation Model for Automatic IE Pattern Acquisition. In: Proceedings of ACL-03, pages 224–231, Sapporo, Japan.
- R. Yangarber, Ralph Grishman, P. Tapanainen, and S. Huttunen. (2000). Automatic Acquisition of Domain Knowledge for Information Extraction. In: Proceedings of COLING 2000, Saarbrücken, Germany.
- R. Yangarber. (2003). Counter-training in the Discovery of Semantic Patterns. In: Proceedings of ACL-03, pages 343–350, Sapporo, Japan.
- F. Xu, D. Kurz, J. Piskorski and S. Schmeier. (2002). A Domain Adaptive Approach to Automatic Acquisition of Domain Relevant Terms and their Relations with Bootstrapping. In: Proceedings of LREC 2002, May 2002.
- F. Xu, H. Uszkoreit and H. Li. (2006). Automatic Event and Relation Detection with Seeds of Varying Complexity. In: Proceedings of AAAI 2006 Workshop Event Extraction and Synthesis, Boston, July, 2006.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2007 ASeedDrivBotUpMLFrameworkForExtrRels | F. Xu H. Uszkoreit H. Li | A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity | Proceedings of ACL 2007 | http://acl.ldc.upenn.edu/P/P07/P07-1074.pdf | 2007 |