Abstract：Chinese functional chunks are defined as a series of non-overlapping, non-nested skeleton segments of a sentence, representing the implicit grammatical relations between the sentence-level predicates and their arguments. In this paper, we proposed two statistical models for parsing four main functional chunks in a sentence. In the chunk boundary detection model, we focus on building the sub models based on SVM algorithm for detecting SP (subject-predicate) and PO (predicate-object) boundaries. In the sequence labeling model, we formulate the chunking task as a sequence labeling problem and base our model on CRF algorithm. By introducing some revision rules, we build a combined parsing model which integrates the advantages of both statistical models and have achieved the best F-Score of 82.93%, 86.58%, 78.46% and 86.64%for subject, predicate, object and adverb functional chunks respectively. Experimental results show that the complex clauses and serial verb structures are the main recognition difficulties.
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2007, Vol. 21 
