Combining Context Features by Canonical Belief Network for Chinese Part-Of-Speech Tagging
نویسندگان
چکیده
Part-Of-Speech(POS) tagging is the essential basis of Natural language processing(NLP). In this paper, we present an algorithm that combines a variety of context features, e.g. the POS tags of the words next to the word a that needs to be tagged and the context lexical information of a by Canonical Belief Network to together determine the POS tag of a. Experiments on a Chinese corpus are conducted to compare our algorithm with the standard HMM-based POS tagging and the POS tagging software ICTCLAS3.0. The experimental results show that our algorithm is more effective.
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Part of speech tagging (POS tagging) is an ongoing research in natural language processing (NLP) applications. The process of classifying words into their parts of speech and labeling them accordingly is known as part-of-speech tagging, POS-tagging, or simply tagging. Parts of speech are also known as word classes or lexical categories. The purpose of POS tagging is determining the grammatical ...
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