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Exploring Predicate-Argument Relations for Named Entity Recognition in the Molecular Biology Domain

TitleExploring Predicate-Argument Relations for Named Entity Recognition in the Molecular Biology Domain
Publication TypeBook Chapter
Year of Publication2005
AuthorsWattarujeekrit, T, Collier, N
EditorHoffmann, A, Motoda, H, Scheffer, T
Book TitleDiscovery Science
Series TitleLecture Notes in Computer Science
Volume3735
Pagination267-280
PublisherSpringer
CityBerlin / Heidelberg
ISBN Number978-3-540-29230-2
Abstract

In this paper, the semantic relationships between a predicate and its arguments in terms of semantic roles are employed to improve lexical-based named entity recognition (NER) in the molecular biology domain. The semantic roles were realized in various sets of syntactic features used by a machine learning model to explore what should be the efficient way in allowing this knowledge to provide the highest positive effect on the NER. The empirical results show that the best feature set consists of predicate’s surface form, predicate’s lemma, voice, and the united feature of subject-object head’s lemma and transitive-intransitive sense. The performance improvement from using these features indicates the advantage of the predicate-argument semantic knowledge on NER. There are still rooms to enhance NER by using this semantic knowledge (e.g. to employ other semantic roles besides agent and theme and to extend the rules for efficient identification of an argument’s boundary).

URLhttp://pdf.aminer.org/000/156/905/exploring_predicate_argument_relations_for_named_entity_recognition_in_the.pdf
DOI10.1007/11563983_23