Abstract
In this paper, we propose to use dependency graphs rather than trees as the interface between a parser and the rule acquisition module of a relation extraction (RE) system. Dependency graphs are much more expressive than trees and can easily be adapted to the output representations of various parsers, in particular those with richer semantics. Our approach is built on top of an existing minimally supervised machine learning system for relation extraction. We extend its original tree-based interface to a graph-based representation. In our experiments, we make use of two different dependency parsers and a deep HPSG parser. As expected, switching to a graph representation for the parsers outputting dependency trees does not have any impact on the RE results. But using the graph-based representation for the extraction with deep HPSG analyses improves both recall and f-score of the RE and enables the system to extract more relation instances of higher arity. Furthermore, we also compare the performance among these parsers with respect to their contribution to the RE task. In general, the robust dependency parsers are good in recall. However, the fine-grained deep syntactic parsing wins when it comes to precision.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Callmeier, U.: Preprocessing and encoding techniques in PET. In: Oepen, S., Flickinger, D., Tsujii, J., Uszkoreit, H. (eds.) Collaborative Language Engineering. A Case Study in Efficient Grammar-based Processing. CSLI Publications, Stanford (2002)
Copestake, A.: Dependency and (R)MRS (December 2008), http://www.cl.cam.ac.uk/~aac10/papers/dmrs.pdf unpublished Draft (December 9, 2008)
Copestake, A., Flickinger, D., Pollard, C., Sag, I.A.: Minimal recursion semantics: An introduction. Research on Language and Computation 3(4) (2005)
Flickinger, D.: On building a more efficient grammar by exploiting types. Natural Language Engineering 6(1) (2000)
Greenwood, M., Stevenson, M., Guo, Y., Harkema, H., Roberts, A.: Automatically Acquiring a Linguistically Motivated Genic Interaction Extraction System. In: Proc. of the 4th Learning Language in Logic Workshop (LLL 2005), Bonn, Germany (2005)
Hara, T., Miyao, Y., Tsujii, J.: Adapting a probabilistic disambiguation model of an HPSG parser to a new domain. In: Dale, R., Wong, K.-F., Su, J., Kwong, O.Y. (eds.) IJCNLP 2005. LNCS (LNAI), vol. 3651, pp. 199–210. Springer, Heidelberg (2005)
Klein, D., Manning, C.D.: Fast exact inference with a factored model for natural language parsing. In: Advances in Neural Information Processing Systems 15 (NIPS 2002), vol. 15. MIT Press, Cambridge (2003)
Lin, D.: Dependency-based evaluation of MINIPAR. In: Abeillé, A. (ed.) Treebanks - Building and Using Parsed Corpora. Kluwer Academic Publishers, Dordrecht (2003)
de Marneffe, M., Maccartney, B., Manning, C.: Generating typed dependency parses from phrase structure parses. In: Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC 2006), Genoa, Italy (2006)
de Marneffe, M., Manning, C.D.: The stanford typed dependencies representation. In: Coling 2008: Proceedings of the Workshop on Cross-Framework and Cross-Domain Parser Evaluation, Manchester, UK (2008)
Miwa, M., Pyysalo, S., Hara, T., Tsujii, J.: A comparative study of syntactic parsers for event extraction. In: Proceedings of the 2010 Workshop on Biomedical Natural Language Processing, Uppsala, Sweden (July 2010)
Miyao, Y., Sagae, K., Satre, R., Matsuzaki, T., Tsujii, J.: Evaluating contributions of natural language parsers to protein-protein interaction extraction. Bioinformatics 25 (2009)
Muslea, I.: Extraction patterns for information extraction tasks: A survey. In: AAAI Workshop on Machine Learning for Information Extraction, Orlando, Florida (July 1999)
Pollard, C.J., Sag, I.A.: Head-Driven Phrase Structure Grammar. University of Chicago Press, Chicago (1994)
Stevenson, M., Greenwood, M.A.: Comparing information extraction pattern models. In: Proceedings of the Workshop on Information Extraction Beyond The Document. Association for Computational Linguistics, Sydney, Australia (July 2006)
Sudo, K., Sekine, S., Grishman, R.: An improved extraction pattern representation model for automatic IE pattern acquisition. In: Proceedings of ACL 2003 (2003)
Sudo, K., Sekine, S., Grishman, R.: Automatic pattern acquisition for japanese information extraction. In: Proceedings of the First International Conference on Human Language Technology Research (HLT 2001), Morristown, NJ, USA (2001)
Toutanova, K., Manning, C.D., Flickinger, D., Oepen, S.: Stochastic HPSG parse selection using the Redwoods corpus. Journal of Research on Language and Computation 3(1) (2005)
Uszkoreit, H., Xu, F., Li, H.: Analysis and improvement of minimally supervised machine learning for relation extraction. In: Horacek, H., Métais, E., Muñoz, R., Wolska, M. (eds.) NLDB 2009. LNCS, vol. 5723, pp. 8–23. Springer, Heidelberg (2010)
Xu, F.: Bootstrapping Relation Extraction from Semantic Seeds. Phd-thesis, Saarland University (2007)
Xu, F., Uszkoreit, H., Li, H.: A seed-driven bottom-up machine learning framework for extracting relations of various complexity. In: Proceedings of ACL 2007. Czech Republic, Prague (2007)
Yangarber, R.: Scenario Customization for Information Extraction. Dissertation, New York University, New York, USA (2001)
Zhang, Y.: Robust Deep Linguistic Processing. Phd-thesis, Saarland University, Saarbruecken, Germany (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Adolphs, P., Xu, F., Li, H., Uszkoreit, H. (2011). Dependency Graphs as a Generic Interface between Parsers and Relation Extraction Rule Learning. In: Bach, J., Edelkamp, S. (eds) KI 2011: Advances in Artificial Intelligence. KI 2011. Lecture Notes in Computer Science(), vol 7006. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24455-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-24455-1_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24454-4
Online ISBN: 978-3-642-24455-1
eBook Packages: Computer ScienceComputer Science (R0)