Chinese Named Entity Recognition with Cascaded Hybrid Model

We propose a high-performance cascaded hybrid model for Chinese NER. Firstly, we use Boosting, a standard and theoretically wellfounded 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.

• Type: Conference
• Year: 2007
• Where: NAACL
• Authors: Xiaofeng Yu
• Keywords: Chinese NER, Computational Linguistics, Hybrid Model, NAACL 2007, State-of-the-art Boosting Model