Kernel Methods for Minimally Supervised WSD
نویسندگان
چکیده
منابع مشابه
Kernel Methods for Minimally Supervised WSD
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 unlabeled data. The results s...
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The lack of positive results on supervised domain adaptation for WSD have cast some doubts on the utility of handtagging general corpora and thus developing generic supervised WSD systems. In this paper we show for the first time that our WSD system trained on a general source corpus (BNC) and the target corpus, obtains up to 22% error reduction when compared to a system trained on the target c...
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ژورنال
عنوان ژورنال: Computational Linguistics
سال: 2009
ISSN: 0891-2017,1530-9312
DOI: 10.1162/coli.2009.35.4.35407