Exploring Segment Representations for Neural Segmentation Models

Many natural language processing (NLP) tasks can be generalized into segmentation problem. In this paper, we combine semi-CRF with neural network to solve NLP segmentation tasks. Our model represents a segment both by composing the input units and embedding the entire segment. We thoroughly study different composition functions and different segment embeddings. We conduct extensive experiments on two typical segmentation tasks: named entity recognition (NER) and Chinese word segmentation (CWS). Experimental results show that our neural semi-CRF model benefits from representing the entire segment and achieves the state-of-the-art performance on CWS benchmark dataset and competitive results on the CoNLL03 NER dataset.

Authors

• Yijia Liu
• Wanxiang Che Research Center for SCIR, Harbin Institute of Technology, Harbin, China
• Jiang Guo
• Bing Qin Harbin Institute of Technology
• Ting Liu Tsinghua University

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