We present a semi-supervised technique for word sense disambiguation that exploits external knowledge acquired in an unsupervised manner. In particular, we use a combination of basic kernel functions to independently estimate syntagmatic and domain similarity, building a set of word-expert classifiers that share a common domain model acquired from a large corpus of unla- beled data. The results show that the proposed approach achieves state-of-the-art performance on a wide range of lexical sample tasks and on the English all-words task of Senseval-3, although it uses a considerably smaller number of training examples than other methods.
Kernel Methods for Minimally Supervised WSD / Giuliano, Claudio; Gliozzo, Alfio Massimiliano; Strapparava, Carlo. - In: COMPUTATIONAL LINGUISTICS. - ISSN 1530-9312. - 35:4(2009), pp. 513-528.
Kernel Methods for Minimally Supervised WSD
Alfio Massimiliano Gliozzo;Carlo Strapparava
2009-01-01
Abstract
We present a semi-supervised technique for word sense disambiguation that exploits external knowledge acquired in an unsupervised manner. In particular, we use a combination of basic kernel functions to independently estimate syntagmatic and domain similarity, building a set of word-expert classifiers that share a common domain model acquired from a large corpus of unla- beled data. The results show that the proposed approach achieves state-of-the-art performance on a wide range of lexical sample tasks and on the English all-words task of Senseval-3, although it uses a considerably smaller number of training examples than other methods.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione