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Neural word segmentation has attracted more and more research interests forits ability to alleviate the effort of feature engineering and utilize theexternal resource by the pre-trained character or word embeddings. In thispaper, we propose a new neural model to incorporate the word-level informationfor Chinese word segmentation. Unlike the previous word-based models, our modelstill adopts the framework of character-based sequence labeling, which hasadvantages on both effectiveness and efficiency at the inference stage. Toutilize the word-level information, we also propose a new long short-termmemory (LSTM) architecture over directed acyclic graph (DAG). Experimentalresults demonstrate that our model leads to better performances than thebaseline models.

DAG-based Long Short-Term Memory for Neural Word Segmentation