research-article

Free access

Entity-focused sentence simplification for relation extraction

Authors:

Jun'ichi TsujiiAuthors Info & Claims

COLING '10: Proceedings of the 23rd International Conference on Computational Linguistics

Pages 788 - 796

Published: 23 August 2010 Publication History

Abstract

Relations between entities in text have been widely researched in the natural language processing and information-extraction communities. The region connecting a pair of entities (in a parsed sentence) is often used to construct kernels or feature vectors that can recognize and extract interesting relations. Such regions are useful, but they can also incorporate unnecessary distracting information. In this paper, we propose a rule-based method to remove the information that is unnecessary for relation extraction. Protein-protein interaction (PPI) is used as an example relation extraction problem. A dozen simple rules are defined on output from a deep parser. Each rule specifically examines the entities in one target interaction pair. These simple rules were tested using several PPI corpora. The PPI extraction performance was improved on all the PPI corpora.

References

[1]

Airola, Antti, Sampo Pyysalo, Jari Björne, Tapio Pahikkala, Filip Ginter, and Tapio Salakoski. 2008. A graph kernel for protein-protein interaction extraction. In Proceedings of the BioNLP 2008 workshop.

Digital Library

[2]

Bunescu, Razvan C. and Raymond J. Mooney. 2005. A shortest path dependency kernel for relation extraction. In HLT '05: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, pages 724--731.

Digital Library

[3]

Bunescu, Razvan C., Ruifang Ge, Rohit J. Kate, Edward M. Marcotte, Raymond J. Mooney, Arun K. Ramani, and Yuk Wah Wong. 2005. Comparative experiments on learning information extractors for proteins and their interactions. Artificial Intelligence in Medicine, 33(2):139--155.

Digital Library

[4]

Chun, Hong-Woo, Yoshimasa Tsuruoka, Jin-Dong Kim, Rie Shiba, Naoki Nagata, Teruyoshi Hishiki, and Jun'ichi Tsujii. 2006. Extraction of gene-disease relations from medline using domain dictionaries and machine learning. In The Pacific Symposium on Biocomputing (PSB), pages 4--15.

[5]

Ding, J., D. Berleant, D. Nettleton, and E. Wurtele. 2002. Mining medline: abstracts, sentences, or phrases? Pacific Symposium on Biocomputing, pages 326--337.

[6]

Doddington, George, Alexis Mitchell, Mark Przybocki, Lance Ramshaw, Stephanie Strassel, and Ralph Weischedel. 2004. The automatic content extraction (ACE) program: Tasks, data, and evaluation. In Proceedings of LREC'04, pages 837--840.

[7]

Dorr, Bonnie, David Zajic, and Richard Schwartz. 2003. Hedge trimmer: A parse-and-trim approach to headline generation. In in Proceedings of Workshop on Automatic Summarization, pages 1--8.

Digital Library

[8]

Fundel, Katrin, Robert Küffner, and Ralf Zimmer. 2006. Relex---relation extraction using dependency parse trees. Bioinformatics, 23(3):365--371.

Digital Library

[9]

Jonnalagadda, Siddhartha and Graciela Gonzalez. 2009. Sentence simplification aids protein-protein interaction extraction. In Proceedings of the 3rd International Symposium on Languages in Biology and Medicine, pages 109--114, November.

[10]

Matsuzaki, Takuya, Yusuke Miyao, and Jun'ichi Tsujii. 2007. Efficient HPSG parsing with supertagging and cfg-filtering. In IJCAI'07: Proceedings of the 20th international joint conference on Artifical intelligence, pages 1671--1676, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.

Digital Library

[11]

Miwa, Makoto, Rune Sætre, Yusuke Miyao, and Jun'ichi Tsujii. 2009. Protein-protein interaction extraction by leveraging multiple kernels and parsers. International Journal of Medical Informatics, June.

[12]

Morante, Roser and Walter Daelemans. 2009. Learning the scope of hedge cues in biomedical texts. In Proceedings of the BioNLP 2009 Workshop, pages 28--36, Boulder, Colorado, June. Association for Computational Linguistics.

Digital Library

[13]

Nédellec, Claire. 2005. Learning language in logic - genic interaction extraction challenge. In Proceedings of the LLL'05 Workshop.

[14]

Pyysalo, Sampo, Filip Ginter, Juho Heimonen, Jari Björne, Jorma Boberg, Jouni Järvinen, and Tapio Salakoski. 2007. BioInfer: A corpus for information extraction in the biomedical domain. BMC Bioinformatics, 8:50.

[15]

Pyysalo, Sampo, Antti Airola, Juho Heimonen, Jari Björne, Filip Ginter, and Tapio Salakoski. 2008. Comparative analysis of five protein-protein interaction corpora. In BMC Bioinformatics, volume 9(Suppl 3), page S6.

[16]

Pyysalo, Sampo, Tomoko Ohta, Jin-Dong Kim, and Jun'ichi Tsujii. 2009. Static relations: a piece in the biomedical information extraction puzzle. In BioNLP '09: Proceedings of the Workshop on BioNLP, pages 1--9, Morristown, NJ, USA. Association for Computational Linguistics.

Digital Library

[17]

Sarawagi, Sunita. 2008. Information extraction. Foundations and Trends in Databases, 1(3):261--377.

Digital Library

[18]

Vanderwende, Lucy, Hisami Suzuki, Chris Brockett, and Ani Nenkova. 2007. Beyond sumbasic: Task-focused summarization with sentence simplification and lexical expansion. Inf. Process. Manage., 43(6):1606--1618.

Digital Library

[19]

Vickrey, David and Daphne Koller. 2008. Sentence simplification for semantic role labeling. In Proceedings of ACL-08: HLT, pages 344--352, Columbus, Ohio, June. Association for Computational Linguistics.

[20]

Zhang, Min, Jie Zhang, Jian Su, and Guodong Zhou. 2006. A composite kernel to extract relations between entities with both flat and structured features. In ACL-44: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, pages 825--832. Association for Computational Linguistics.

Digital Library

Cited By

Al-Thanyyan SAzmi A(2021)Automated Text SimplificationACM Computing Surveys10.1145/344269554:2(1-36)Online publication date: 5-Mar-2021
https://dl.acm.org/doi/10.1145/3442695
Kido YYokono HTopić GAizawa AVanoirbeek CGenevès P(2015)Document Layout Optimization with Automated ParaphrasingProceedings of the 2015 ACM Symposium on Document Engineering10.1145/2682571.2797095(13-16)Online publication date: 8-Sep-2015
https://dl.acm.org/doi/10.1145/2682571.2797095
Wei CLeaman RLu ZBaldi PWang W(2014)SimConceptProceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/2649387.2649420(138-146)Online publication date: 20-Sep-2014
https://dl.acm.org/doi/10.1145/2649387.2649420
Show More Cited By

Entity-focused sentence simplification for relation extraction
1. Computing methodologies
  1. Artificial intelligence
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

Recommendations

Cross-sentence N-ary Relation Extraction using Entity Link and Discourse Relation
CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

This paper presents an efficient method of extracting n-ary relations from multiple sentences which is called Entity-path and Discourse relation-centric Relation Extractor (EDCRE). Unlike previous approaches, the proposed method focuses on an entity ...
REKER: Relation Extraction with Knowledge of Entity and Relation
Natural Language Processing and Chinese Computing
Abstract
Relation Extraction (RE) is an important task to mine knowledge from massive text corpus. Existing relation extraction methods usually purely rely on the textual information of sentences to predict the relations between entities. The useful ...
A Flexible Text Mining System for Entity and Relation Extraction in PubMed
DTMBIO '15: Proceedings of the ACM Ninth International Workshop on Data and Text Mining in Biomedical Informatics

Due to an enormous number of scientific publications that cannot be handled manually, there is a rising interest in text-mining techniques for automated information extraction, especially in the biomedical field. Such techniques provide effective means ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image DL Hosted proceedings

COLING '10: Proceedings of the 23rd International Conference on Computational Linguistics

August 2010

1408 pages

General Chair:
Aravind K. Joshi
University of Pennsylvania
,
Program Chairs:
Chu-Ren Huang
The Hong Kong Polytechnic University
,
Dan Jurafsky
Stanford University

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 23 August 2010

Qualifiers

Research-article

Acceptance Rates

Overall Acceptance Rate 1,537 of 1,537 submissions, 100%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
315
Total Downloads

Downloads (Last 12 months)20
Downloads (Last 6 weeks)9

Reflects downloads up to 03 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Al-Thanyyan SAzmi A(2021)Automated Text SimplificationACM Computing Surveys10.1145/344269554:2(1-36)Online publication date: 5-Mar-2021
https://dl.acm.org/doi/10.1145/3442695
Kido YYokono HTopić GAizawa AVanoirbeek CGenevès P(2015)Document Layout Optimization with Automated ParaphrasingProceedings of the 2015 ACM Symposium on Document Engineering10.1145/2682571.2797095(13-16)Online publication date: 8-Sep-2015
https://dl.acm.org/doi/10.1145/2682571.2797095
Wei CLeaman RLu ZBaldi PWang W(2014)SimConceptProceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/2649387.2649420(138-146)Online publication date: 20-Sep-2014
https://dl.acm.org/doi/10.1145/2649387.2649420
Lima RBatista JFerreira RFreitas FLins RSimske SRiss MSimske SRönnau S(2014)Transforming graph-based sentence representations to alleviate overfitting in relation extractionProceedings of the 2014 ACM symposium on Document engineering10.1145/2644866.2644875(53-62)Online publication date: 16-Sep-2014
https://dl.acm.org/doi/10.1145/2644866.2644875
Shiguihara-Juárez Pde Andrade Lopes A(2013)Learning bayesian network using parse trees for extraction of protein-protein interactionProceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 210.1007/978-3-642-37256-8_29(347-358)Online publication date: 24-Mar-2013
https://dl.acm.org/doi/10.1007/978-3-642-37256-8_29
Coster WKauchak DFilippova KWan S(2011)Learning to simplify sentences using WikipediaProceedings of the Workshop on Monolingual Text-To-Text Generation10.5555/2107679.2107680(1-9)Online publication date: 24-Jun-2011
https://dl.acm.org/doi/10.5555/2107679.2107680
Coster WKauchak DLin D(2011)Simple English WikipediaProceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 210.5555/2002736.2002865(665-669)Online publication date: 19-Jun-2011
https://dl.acm.org/doi/10.5555/2002736.2002865

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents