Adapting a State-of-the-art Anaphora Resolution System for Resource-poor Language
Utpal Kumar Sikdar, Asif Ekbal, Sriparna Saha, Olga Uryupina, Massimo Poesio · Institutional Research Information System (Università degli Studi di Trento) · 2013
In this paper we present our work on adapting a state-of-the-art anaphora res-olution system for a resource poor lan-guage, namely Bengali. Performance of any anaphoric resolver greatly depends on the quality of a high accurate mention de-tector. We develop a number of mod-els for mention detection based on heuris-tics and machine learning. Our exper-iments show that, a language-dependent system can attain reasonably good perfor-mance 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 experi-ments with other available scorers show the F-measure values of 59.47%, 49.83%,