Web-scale N-gram models for lexical disambiguation
IJCAI(2009)
摘要
Web-scale data has been used in a diverse range of language research. Most of this research has used web counts for only short, fixed spans of context. We present a unified view of using web counts for lexical disambiguation. Unlike previous approaches, our supervised and unsupervised systems combine information from multiple and overlapping segments of context. On the tasks of preposition selection and context-sensitive spelling correction, the supervised system reduces disambiguation error by 20-24% over the current state-of-the-art.
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关键词
disambiguation error,web count,web-scale n-gram model,web-scale data,diverse range,supervised system,language research,context-sensitive spelling correction,fixed span,lexical disambiguation,overlapping segment
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