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The knowledge acquisition bottleneck is perhaps the major impediment to solving the word sense disambiguation (WSD) problem. Unsupervised learning methods rely on knowledge about word senses, which is barely formulated in dictionaries and lexical databases. Supervised learning methods depend heavily on the existence of manually annotated examples for every word sense, a requisite that can be met only for a handful of words for testing purposes, as it is done in the Senseval exercises. ==Existing methods== Therefore, one of the most promising trends in WSD research is using the largest corpus ever accessible, the World Wide Web, to acquire lexical information automatically.〔Kilgarriff, A.; G. Grefenstette. 2003. (Introduction to the special issue on the Web as corpus ). Computational Linguistics 29(3)〕 WSD has been traditionally understood as an intermediate language engineering technology which could improve applications such as information retrieval (IR). In this case, however, the reverse is also true: Web search engines implement simple and robust IR techniques that can be successfully used when mining the Web for information to be employed in WSD. The most direct way of using the Web (and other corpora) to enhance WSD performance is the automatic acquisition of sense-tagged corpora, the fundamental resource to feed supervised WSD algorithms. Although this is far from being commonplace in the WSD literature, a number of different and effective strategies to achieve this goal have already been proposed. Some of these strategies are: * acquisition by direct Web searching (searches for monosemous synonyms, hypernyms, hyponyms, parsed gloss' words, etc.), * Yarowsky algorithm (bootstrapping), * acquisition via Web directories, and * acquisition via cross-language meaning evidences. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Automatic acquisition of sense-tagged corpora」の詳細全文を読む スポンサード リンク
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