Chinese named entity recognition with cascaded hybrid model

Authors:
Xiaofeng Yu
Affiliations:
The Chinese University of Hong Kong, N.T., Hong Kong
Venue:
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Year:
2007

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Abstract

We propose a high-performance cascaded hybrid model for Chinese NER. Firstly, we use Boosting, a standard and theoretically well-founded machine learning method to combine a set of weak classifiers together into a base system. Secondly, we introduce various types of heuristic human knowledge into Markov Logic Networks (MLNs), an effective combination of first-order logic and probabilistic graphical models to validate Boosting NER hypotheses. Experimental results show that the cascaded hybrid model significantly outperforms the state-of-the-art Boosting model.