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Exploring the effectiveness of linguistic knowledge for biographical relation extraction†

Published online by Cambridge University Press:  18 October 2013

MARCOS GARCIA  and
PABLO GAMALLO
MARCOS GARCIA
Affiliation:
Centro Singular de Investigación en Tecnoloxías da Información (CITIUS), University of Santiago de Compostela, Coruña, Spain e-mail: [email protected], [email protected]
PABLO GAMALLO
Affiliation:
Centro Singular de Investigación en Tecnoloxías da Información (CITIUS), University of Santiago de Compostela, Coruña, Spain e-mail: [email protected], [email protected]
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Abstract

Machine learning techniques have been implemented to extract instances of semantic relations using diverse features based on linguistic knowledge, such as tokens, lemmas, PoS-tags, or dependency paths. However, there has been little work aiming to know which of these features works better in the relation extraction task, and less in languages other than English. In this paper, various features representing different levels of linguistic knowledge are systematically evaluated for biographical relation extraction. The effectiveness of these features was measured by training several supervised classifiers that only differ in the type of linguistic knowledge used to define their features. The experiments performed in this paper show that some basic linguistic knowledge (provided by lemmas and their combination in bigrams) behaves better than other complex features, such as those based on syntactic analysis. Furthermore, some feature combinations using different levels of analysis are proposed in order (i) to avoid feature overlapping as well as (ii) to evaluate the use of computationally inexpensive and widespread tools such as tokenization and lemmatization. This paper also describes two new freely available corpora for biographical relation extraction in Portuguese and Spanish, built by means of a distant-supervision strategy. Experiments were performed with five semantic relations and two languages, using these corpora.

Type
Articles
Information
Natural Language Engineering , Volume 21 , Issue 4 , August 2015 , pp. 519 - 551
Copyright
Copyright © Cambridge University Press 2013 

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