tailieunhanh - Báo cáo khoa học: "Combining Orthogonal Monolingual and Multilingual Sources of Evidence for All Words WSD"

Word Sense Disambiguation remains one of the most complex problems facing computational linguists to date. In this paper we present a system that combines evidence from a monolingual WSD system together with that from a multilingual WSD system to yield state of the art performance on standard All-Words data sets. The monolingual system is based on a modification of the graph based state of the art algorithm In-Degree. | Combining Orthogonal Monolingual and Multilingual Sources of Evidence for All Words WSD Weiwei Guo Computer Science Department Columbia University New York NY 10115 weiwei@ Mona Diab Center for Computational Learning Systems Columbia University New York NY 10115 mdiab@ Abstract Word Sense Disambiguation remains one of the most complex problems facing computational linguists to date. In this paper we present a system that combines evidence from a monolingual WSD system together with that from a multilingual WSD system to yield state of the art performance on standard All-Words data sets. The monolingual system is based on a modification of the graph based state of the art algorithm In-Degree. The multilingual system is an improvement over an AllWords unsupervised approach SALAAM. SALAAM exploits multilingual evidence as a means of disambiguation. In this paper we present modifications to both of the original approaches and then their combination. We finally report the highest results obtained to date on the SENSEVAL 2 standard data set using an unsupervised method we achieve an overall F measure of using a voting scheme. 1 Introduction Despite advances in natural language processing NLP Word Sense Disambiguation WSD is still considered one of the most challenging problems in the field. Ever since the field s inception WSD has been perceived as one of the central problems in NLP. WSD is viewed as an enabling technology that could potentially have far reaching impact on NLP applications in general. We are starting to see the beginnings of a positive effect of WSD in NLP applications such as Machine Translation Carpuat and Wu 2007 Chan et al. 2007 . Advances in WSD research in the current millennium can be attributed to several key factors the availability of large scale computational lexical resources such as WordNets Fellbaum 1998 Miller 1990 the availability of large scale corpora the existence and dissemination of standardized