Abstract
Handcrafted rule based systems attain a high level of performance but constructing rules is a time consuming work and low frequency patterns are easy to be neglected. This paper presents a hybrid approach, which combines a machine learning method and a rule based method, to improve our Chinese NE system’s efficiency. We describe a bootstrapping algorithm that extracts patterns and generates semantic lexicons simultaneously. After the use of new patterns 14% more person names are extracted by our system.
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© 2002 Springer-Verlag Berlin Heidelberg
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Fang, X., Sheng, H. (2002). A Hybrid Approach for Chinese Named Entity Recognition. In: Lange, S., Satoh, K., Smith, C.H. (eds) Discovery Science. DS 2002. Lecture Notes in Computer Science, vol 2534. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36182-0_28
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DOI: https://doi.org/10.1007/3-540-36182-0_28
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