We present a new minimally-supervised framework for performing domain-driven Word Sense Disambiguation (WSD). Glossaries for several domains are iteratively acquired from the Web by means of a bootstrapping technique. The acquired glosses are then used as the sense inventory for fully-unsupervised domain WSD. Our experiments, on new and gold-standard datasets, show that our wide-coverage framework enables high-performance results on dozens of domains at a coarse and fine-grained level. © 2012 Association for Computational Linguistics.
A new minimally-supervised framework for domain word sense disambiguation / Faralli, Stefano; Navigli, Roberto. - STAMPA. - (2012), pp. 1411-1422. (Intervento presentato al convegno 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012 tenutosi a Jeju Island nel 12 July 2012 through 14 July 2012).
A new minimally-supervised framework for domain word sense disambiguation
FARALLI, Stefano;NAVIGLI, ROBERTO
2012
Abstract
We present a new minimally-supervised framework for performing domain-driven Word Sense Disambiguation (WSD). Glossaries for several domains are iteratively acquired from the Web by means of a bootstrapping technique. The acquired glosses are then used as the sense inventory for fully-unsupervised domain WSD. Our experiments, on new and gold-standard datasets, show that our wide-coverage framework enables high-performance results on dozens of domains at a coarse and fine-grained level. © 2012 Association for Computational Linguistics.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
