NDLI: Relation extraction and the influence of automatic named-entity recognition

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Relation extraction and the influence of automatic named-entity recognition

Content Provider	ACM Digital Library
Author	Giuliano, Claudio Lavelli, Alberto Romano, Lorenza
Copyright Year	2007
Abstract	We present an approach for extracting relations between named entities from natural language documents. The approach is based solely on shallow linguistic processing, such as tokenization, sentence splitting, part-of-speech tagging, and lemmatization. It uses a combination of kernel functions to integrate two different information sources: (i) the whole sentence where the relation appears, and (ii) the local contexts around the interacting entities. We present the results of experiments on extracting five different types of relations from a dataset of newswire documents and show that each information source provides a useful contribution to the recognition task. Usually the combined kernel significantly increases the precision with respect to the basic kernels, sometimes at the cost of a slightly lower recall. Moreover, we performed a set of experiments to assess the influence of the accuracy of named-entity recognition on the performance of the relation-extraction algorithm. Such experiments were performed using both the correct named entities (i.e., those manually annotated in the corpus) and the noisy named entities (i.e., those produced by a machine learning-based named-entity recognizer). The results show that our approach significantly improves the previous results obtained on the same dataset.
Starting Page	1
Ending Page	26
Page Count	26
File Format	PDF
ISSN	15504875
e-ISSN	15504883
DOI	10.1145/1322391.1322393
Volume Number	5
Issue Number	1
Journal	ACM Transactions on Speech and Language Processing (TSLP)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-12-12
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Information extraction Kernel methods Named-entity recognition Relation extraction
Content Type	Text
Resource Type	Article
Subject	1700/1701 Computational Mathematics

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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