Global discriminative model for dependency parsing in NLP pipeline

Abstract

Dependency parsing, which is a fundamental task in Natural Language Processing (NLP), has attracted a lot of interest in recent years. In general, it is a module in an NLP pipeline together with word segmentation and Part-Of-Speech (POS) tagging in real Chinese NLP application. The NLP pipeline, which is a cascade system, will lead to error propagation for the parsing. This paper proposes a global discriminative re-ranking model using non-local features from word segmentation, POS tagging and dependency parsing to re-rank the parse trees produced by an N-best enhanced NLP pipeline. Experimental results indicate that the proposed model can improve the performance of dependency parsing as well as word segmentation and POS tagging in an NLP pipeline.