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Web-Scale N-gram Models for Lexical Disambiguation

2019-11-14 | |  59 |   41 |   0

Abstract Web-scale data has been used in a diverse range of language research. Most of this research has used web counts for only short, fifixed spans of context. We present a unifified 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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