ABSTRACT
This paper proposes a novel composite kernel for relation extraction. The composite kernel consists of two individual kernels: an entity kernel that allows for entity-related features and a convolution parse tree kernel that models syntactic information of relation examples. The motivation of our method is to fully utilize the nice properties of kernel methods to explore diverse knowledge for relation extraction. Our study illustrates that the composite kernel can effectively capture both flat and structured features without the need for extensive feature engineering, and can also easily scale to include more features. Evaluation on the ACE corpus shows that our method outperforms the previous best-reported methods and significantly out-performs previous two dependency tree kernels for relation extraction.
- ACE. 2002-2005. The Automatic Content Extraction Projects. http://www.ldc.upenn.edu/Projects/ACE/Google Scholar
- Basili R., Cammisa M. and Moschitti A. 2005. A Semantic Kernel to classify text with very few training examples. ICML-2005Google Scholar
- Bunescu R. C. and Mooney R. J. 2005. A Shortest Path Dependency Kernel for Relation Extraction. EMNLP-2005 Google ScholarDigital Library
- Charniak E. 2001. Immediate-head Parsing for Language Models. ACL-2001 Google ScholarDigital Library
- Collins M. and Duffy N. 2001. Convolution Kernels for Natural Language. NIPS-2001Google Scholar
- Culotta A. and Sorensen J. 2004. Dependency Tree Kernel for Relation Extraction. ACL-2004 Google ScholarDigital Library
- Haussler D. 1999. Convolution Kernels on Discrete Structures. Technical Report UCS-CRL-99-10, University of California, Santa Cruz.Google Scholar
- Joachims T. 1998. Text Categorization with Support Vecor Machine: learning with many relevant features. ECML-1998 Google ScholarDigital Library
- Kambhatla N. 2004. Combining lexical, syntactic and semantic features with Maximum Entropy models for extracting relations. ACL-2004 (poster) Google ScholarDigital Library
- Lodhi H., Saunders C., Shawe-Taylor J., Cristianini N. and Watkins C. 2002. Text classification using string kernel. Journal of Machine Learning Research, 2002(2):419--444 Google ScholarDigital Library
- Miller S., Fox H., Ramshaw L. and Weischedel R. 2000. A novel use of statistical parsing to extract information from text. NAACL-2000 Google ScholarDigital Library
- Moschitti A. 2004. A Study on Convolution Kernels for Shallow Semantic Parsing. ACL-2004 Google ScholarDigital Library
- MUC. 1987-1998. http://www.itl.nist.gov/iaui/894.02/related_projects/muc/Google Scholar
- Schölkopf B. and Smola A. J. 2001. Learning with Kernels: SVM, Regularization, Optimization and Beyond. MIT Press, Cambridge, MA 407--423 Google ScholarDigital Library
- Suzuki J., Hirao T., Sasaki Y. and Maeda E. 2003. Hierarchical Directed Acyclic Graph Kernel: Methods for Structured Natural Language Data. ACL-2003 Google ScholarDigital Library
- Zelenko D., Aone C. and Richardella A. 2003. Kernel Methods for Relation Extraction. Journal of Machine Learning Research. 2003(2):1083--1106 Google ScholarDigital Library
- Zhao S. B. and Grishman R. 2005. Extracting Relations with Integrated Information Using Kernel Methods. ACL-2005 Google ScholarDigital Library
- Zhou G. D., Su J, Zhang J. and Zhang M. 2005. Exploring Various Knowledge in Relation Extraction. ACL-2005Google Scholar
- A composite kernel to extract relations between entities with both flat and structured features
Recommendations
Finite-state transducer cascades to extract named entities in texts
Implementation and application automataA lot of Named Entity Extraction Systems were created in English thanks to the impulse of MUC conferences. This article describes a Finite-State Transducer Cascade for the extraction of named entities in French journalistic texts. Finite-State Cascades ...
Discovering Missing Semantic Relations between Entities in Wikipedia
ISWC '13: Proceedings of the 12th International Semantic Web Conference - Part IWikipedia's infoboxes contain rich structured information of various entities, which have been explored by the DBpedia project to generate large scale Linked Data sets. Among all the infobox attributes, those attributes having hyperlinks in its values ...
An Attention-based Model for Joint Extraction of Entities and Relations with Implicit Entity Features
WWW '19: Companion Proceedings of The 2019 World Wide Web ConferenceExtracting entities and relations is critical to the understanding of massive text corpora. Recently, neural joint models have shown promising results for this task. However, the entity features are not effectively used in these joint models. In this ...
Comments