In this paper we present our work on adapting a state-of-the-art anaphora resolution system for a resource poor language, namely Bengali. Performance of any anaphoric resolver greatly depends on the quality of a high accurate mention detector. We develop a number of models for mention detection based on heuristics and machine learning. Our experiments show that, a language-dependent system can attain reasonably good performance when re-trained on a new language with a proper subset of features. The system yields the MUC recall, precision and F-measure values of 57.80%, 79.00% and 66.70%, respectively. Our experiments with other available scorers show the F-measure values of 59.47%, 49.83%, 31.81% and 70.82% for BCUB, CEAFM, CEAFE and BLANC, respectively

Adapting a State-of-the- art Anaphora Resolution System for Resource-poor Language

Uryupina, Olga;Poesio, Massimo
2013-01-01

Abstract

In this paper we present our work on adapting a state-of-the-art anaphora resolution system for a resource poor language, namely Bengali. Performance of any anaphoric resolver greatly depends on the quality of a high accurate mention detector. We develop a number of models for mention detection based on heuristics and machine learning. Our experiments show that, a language-dependent system can attain reasonably good performance when re-trained on a new language with a proper subset of features. The system yields the MUC recall, precision and F-measure values of 57.80%, 79.00% and 66.70%, respectively. Our experiments with other available scorers show the F-measure values of 59.47%, 49.83%, 31.81% and 70.82% for BCUB, CEAFM, CEAFE and BLANC, respectively
2013
International Joint Conference on Natural Language Processing
USA
ACL - Association for Computational Linguistic
U., Sikdar; A., Ekbal; S., Saha; Uryupina, Olga; Poesio, Massimo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/66845
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