Word Segmentation and Named Entity Recognition for SIGHAN Bakeoff3
نویسندگان
چکیده
We have participated in three open tracks of Chinese word segmentation and named entity recognition tasks of SIGHAN Bakeoff3. We take a probabilistic feature based Maximum Entropy (ME) model as our basic frame to combine multiple sources of knowledge. Our named entity recognizer achieved the highest F measure for MSRA, and word segmenter achieved the medium F measure for MSRA. We find effective combining of the external multi-knowledge is crucial to improve performance of word segmentation and named entity recognition.
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تاریخ انتشار 2006