Premium
SORTAL ANAPHORA RESOLUTION IN MEDLINE ABSTRACTS
Author(s) -
Torii Manabu,
VijayShanker K.
Publication year - 2007
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2007.00292.x
Subject(s) - demonstrative , determiner , anaphora (linguistics) , computer science , natural language processing , resolution (logic) , phrase , artificial intelligence , noun phrase , linguistics , noun , philosophy
This paper reports our investigation of machine learning methods applied to anaphora resolution for biology texts, particularly paper abstracts. Our primary concern is the investigation of features and their combinations for effective anaphora resolution. In this paper, we focus on the resolution of demonstrative phrases and definite determiner phrases, the two most prevalent forms of anaphoric expressions that we find in biology research articles. Different resolution models are developed for demonstrative and definite determiner phrases. Our work shows that models may be optimized differently for each of the phrase types. Also, because a significant number of definite determiner phrases are not anaphoric, we induce a model to detect anaphoricity, i.e., a model that classifies phrases as either anaphoric or nonanaphoric. We propose several novel features that we call highlighting features , and consider their utility particularly for processing paper abstracts. The system using the highlighting features achieved accuracies of 78% and 71% for demonstrative phrases and definite determiner phrases, respectively. The use of the highlighting features reduced the error rate by about 10%.