DAG-based Long Short-Term Memory for Neural Word Segmentation
This work addresses Chinese word segmentation for natural language processing applications, representing an incremental improvement by enhancing existing methods with a novel LSTM architecture.
The paper tackled the problem of Chinese word segmentation by proposing a new neural model that incorporates word-level information while maintaining a character-based sequence labeling framework, resulting in better performance than baseline models.
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.