Abstract
This paper proposes a probabilistic feature based Maximum Entropy (ME) model for Chinese named entity recognition. Where, probabilistic feature functions are used instead of binary feature functions, it is one of the several differences between this model and the most of the previous ME based model. We also explore several new features in our model, which includes confidence functions, position of features etc. Like those in some previous works, we use sub-models to model Chinese Person Names, Foreign Names, location name and organization name respectively, but we bring some new techniques in these sub-models. Experimental results show our ME model combining above new elements brings significant improvements.
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhang, S., Wang, X., Wen, J., Qin, Y., Zhong, Y. (2006). A Probabilistic Feature Based Maximum Entropy Model for Chinese Named Entity Recognition. In: Matsumoto, Y., Sproat, R.W., Wong, KF., Zhang, M. (eds) Computer Processing of Oriental Languages. Beyond the Orient: The Research Challenges Ahead. ICCPOL 2006. Lecture Notes in Computer Science(), vol 4285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11940098_20
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DOI: https://doi.org/10.1007/11940098_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49667-0
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