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Differential evolution-based feature selection technique for anaphora resolution

Sikdar, UK and Ekbal, A and Saha, S and Uryupina, O and Poesio, M (2015) 'Differential evolution-based feature selection technique for anaphora resolution.' Soft Computing, 19 (8). 2149 - 2161. ISSN 1432-7643

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Abstract

© 2014, Springer-Verlag Berlin Heidelberg. In this paper a differential evolution (DE)-based feature selection technique is developed for anaphora resolution in a resource-poor language, namely Bengali. We discuss the issues of adapting a state-of-the-art English anaphora resolution system for a resource-poor language like Bengali. Performance of any anaphoric resolver greatly depends on the quality of a high accurate mention detector and the use of appropriate features for anaphora resolution. We develop a number of models for mention detection based on machine learning and heuristics. In anaphora resolution there is no globally accepted metric for measuring the performance, and each of them such as MUC, $$\hbox {B}^{3}$$B3, CEAF, Blanc exhibit significantly different behaviors. Our proposed feature selection technique determines the near-optimal feature set by optimizing each of these evaluation metrics. Experiments show how a language-dependent system (designed primarily for English) can attain reasonably good performance level when re-trained and tested on a new language with a proper subset of features. Evaluation results yield the F-measure values of 66.70, 59.47, 51.56, 33.08 and 72.75 % for MUC, B3, CEAFM, CEAFE and BLANC, respectively.

Item Type: Article
Subjects: P Language and Literature > P Philology. Linguistics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 04 Feb 2015 11:52
Last Modified: 23 Jan 2019 06:15
URI: http://repository.essex.ac.uk/id/eprint/12581

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