Automatic identification of predicate heads in Chinese sentences

We propose an effective approach to automatically identify predicate heads in Chinese sentences based on statistical pre-processing and rule-based post-processing. In the pre-processing stage, the maximal noun phrases in a sentence are recognized and replaced by “NP” labels to simplify the sentence structure. Then a CRF model is trained to recognize the predicate heads of this simplified sentence. In the post-processing stage, a rule base is built according to the grammatical features of predicate heads. It is then utilized to correct the preliminary recognition results. Experimental results show that our approach is feasible and effective, and its accuracy achieves 89.14% on Tsinghua Chinese Treebank.