Abstract
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, \(\hbox {B}^{3}\), 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, B 3, CEAFM, CEAFE and BLANC, respectively.
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Notes
Here B, I and O denote the beginning, internal and outside the token representing the entity mention.
Henceforth all the Bengali glosses are written in ITRANS notations available at http://www.aczoom.com/itrans/.
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Sikdar, U.K., Ekbal, A., Saha, S. et al. Differential evolution-based feature selection technique for anaphora resolution. Soft Comput 19, 2149–2161 (2015). https://doi.org/10.1007/s00500-014-1397-3
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DOI: https://doi.org/10.1007/s00500-014-1397-3