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Word segmentation is usually recognized as the first step for many Chinese natural language processing tasks, yet its impact on these subsequent tasks is relatively under-studied. For example, how to solve the mismatch problem when applying an existing word segmenter to new data? Does a better word segmenter yield a better subsequent NLP task performance? In this work, we conduct an initial attempt to answer these questions on two related subsequent tasks: semantic slot filling in spoken language understanding and named entity recognition. We propose three techniques to solve the mismatch problem: using word segmentation outputs as additional features, adaptation with partial-learning and taking advantage of n-best word segmentation list. Experimental results demonstrate the effectiveness of these techniques for both tasks and we achieve an error reduction of about 11% for spoken language understanding and 24% for named entity recognition over the baseline systems.

An Empirical Study of Automatic Chinese Word Segmentation for Spoken Language Understanding and Named Entity Recognition