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The Brill tagger is an inductive method for part-of-speech tagging. It was described and invented by Eric Brill in his 1995 PhD thesis. It can be summarized as an "error-driven transformation-based tagger". It is * a form of supervised learning, which aims to minimize error * transformation-based in the sense that a tag is assigned to each word and changed using a set of predefined rules. Note: If the word is known, it first assigns the most frequent tag, or if the word is unknown, it naively assigns the tag "noun" to it. Applying over and over these rules, changing the incorrect tags, a quite high accuracy is achieved. This approach ensures that valuable information such as morphosyntact construct of words are employed in an automatic tagging process. == Algorithm == The algorithm starts with initialization, which is the assignment of tags based on their probability for each word (for example, "dog" is more often a noun than a verb). Then "patches" are determined via rules that correct (probable) tagging errors made in the initialization phase:〔Eric Brill. 1992. A simple rule-based part of speech tagger. In Proceedings of the third conference on Applied natural language processing (ANLC '92). Association for Computational Linguistics, Stroudsburg, PA, USA, 152-155. DOI=10.3115/974499.974526 http://dx.doi.org/10.3115/974499.974526〕 * Initialization: * * Known words (in vocabulary): assigning the most frequent tag associated to a form of the word * * Unknown word 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「Brill tagger」の詳細全文を読む スポンサード リンク
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