Building a Language Model for POS Tagging
نویسندگان
چکیده
Part-of-speech tagging based on a probabilistic model requires ne tuning of the language model for successful results. Though numerous part-of-speech taggers based on this technology have now been developed for a range of natural languages, little is reported on how the model was tuned. Elaborating such a model for a new language or for a new set of tags requires appropriate tools to support the iterative reenement cycle and to successively evaluate the results. In this paper we present a exible set of tagging tools for developing a new language model, adapting an existing model to a new corpus and experimenting with diierent lexical input and corpus tagsets. 1 Background The interest in part-of-speech (POS) tagging has increased considerably over the past decade and successful systems have been reported on for a number of languages (cf. The focus has been on attaining a high level of accuracy (at least 95%) with a given tagset rather than on exible general purpose tools. The taggers are typically developed for a single natural language and incorporate a number of language-speciic assumptions. The resources they use, including the lexical lists and the corpus tags are often embedded in the program and diicult to extend or modify. POS tagging based on a Hidden Markov model (HMM) is now commonly accepted as an eeective technique for a range of natural languages. The adequacy and accuracy of a tagger based on such a model is not inherent in the technique employed, nor
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