  Neural word segmentation has attracted more and more research interests for
its ability to alleviate the effort of feature engineering and utilize the
external resource by the pre-trained character or word embeddings. In this
paper, we propose a new neural model to incorporate the word-level information
for Chinese word segmentation. Unlike the previous word-based models, our model
still adopts the framework of character-based sequence labeling, which has
advantages on both effectiveness and efficiency at the inference stage. To
utilize the word-level information, we also propose a new long short-term
memory (LSTM) architecture over directed acyclic graph (DAG). Experimental
results demonstrate that our model leads to better performances than the
baseline models.
