Part of Speech Tagging for English Text Data
نویسنده
چکیده
A variety of Natural Language Processing (NLP) tasks, such as named entity recognition, stemming and question answering, benefit from knowledge of the words syntactic categories or Partof-Speech (POS) [4][6]. POS taggers have been successfully applied to assign a single best POS to every word in a corpus [2][5][12]. This paper reports on the implementation and empiric comparison of three supervised, stochastic tagging approaches (Unigram Model, Hidden Markov Model, Viterbi algorithm). The presented comparison not only quantifies the tagging accuracy achieved by the Viterbi algorithm (93.9% on average), but also determines the partial accuracy gain that different components represented in Viterbi account for.
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