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TBL-improved non-deterministic segmentation and tagging for a Chinese parser
Conferences & Talks
28 March 2009
Although much progress has been made recently in word segmentation and POS tagging for Chinese, the output of current state-of-the-art systems is too inaccurate to allow for syntactic analysis based on it. We present an experiment in improving the output of an off-the-shelf module that performs segmentation and tagging, the tokenizer-tagger from Beijing University (PKU). Our approach is based on transformation-based learning (TBL). Unlike in other TBL-based approaches to the problem, however, both obligatory and optional transformation rules are learned, so that the final system can output multiple segmentation and POS tagging analyses for a given input.
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