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The Impact of Enriched Linguistic Annotation on the Performance of Extracting Relation Triples

Kim, Sanghee, Lewis, Paul and Martinez, Kirk, Gelbukh, Alexander(ed.) (2004) The Impact of Enriched Linguistic Annotation on the Performance of Extracting Relation Triples Computational Linguistics and Intelligent Text Processing, Lectur, (2945), pp. 547-558.

Record type: Article

Abstract

A relation extraction system recognises pre-defined relation types between two identified entities from natural language documents. It is important for a task of automatically locating missing instances in knowledge base where the instance is represented as a triple (‘entity – relation – entity’). A relation entry specifies a set of rules associated with the syntactic and semantic conditions under which appropriate relations would be extracted. Manually creating such rules requires knowledge from information experts and moreover, it is a time-consuming and error-prone task when the input sentences have little consistency in terms of structures and vocabularies. In this paper, we present an approach for applying a symbolic learning algorithm to sentences in order to automatically induce the extraction rules which then successfully classify a new sentence. The proposed approach takes into account semantic attributes (e.g., semantically close words) as well as linguistic features(entity types) in generalising common patterns among the sentences which enable the system to cope better with syntactically different but semantically similar sentences. Not only does this increase the number of relations extracted, but it also improves the accuracy in extracting relations by adding features which might not be discovered only with syntactic analysis. Experimental results show that this approach is effective on the sentences of the Web documents obtaining 17% higher precision and 34% higher recall values.

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Published date: February 2004
Keywords: relation extraction, information extraction, inductive logic programming
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 258880
URI: http://eprints.soton.ac.uk/id/eprint/258880
PURE UUID: 87a3e4cb-a0af-4ede-9135-df0c2bee7cc6
ORCID for Kirk Martinez: ORCID iD orcid.org/0000-0003-3859-5700

Catalogue record

Date deposited: 25 Feb 2004
Last modified: 18 Jul 2017 09:29

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Contributors

Author: Sanghee Kim
Author: Paul Lewis
Author: Kirk Martinez ORCID iD
Editor: Alexander Gelbukh

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