  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 dataset.
