ML p(r)ior | A syntax-based part-of-speech analyser

A syntax-based part-of-speech analyser

9502012 | cmp-lg
There are two main methodologies for constructing the knowledge base of a natural language analyser: the linguistic and the data-driven. Recent state-of-the-art part-of-speech taggers are based on the data-driven approach. Because of the known feasibility of the linguistic rule-based approach at related levels of description, the success of the data-driven approach in part-of-speech analysis may appear surprising. In this paper, a case is made for the syntactic nature of part-of-speech tagging. A new tagger of English that uses only linguistic distributional rules is outlined and empirically evaluated. Tested against a benchmark corpus of 38,000 words of previously unseen text, this syntax-based system reaches an accuracy of above 99%. Compared to the 95-97% accuracy of its best competitors, this result suggests the feasibility of the linguistic approach also in part-of-speech analysis.

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