This paper proposes an embedding matching approach to Chinese word segmentation, which generalizes the traditional sequence labeling framework and takes advantage of distributed representations. The training and prediction algorithms have linear-time complexity. Based on the proposed model, a greedy segmenter is developed and evaluated on benchmark corpora. Experiments show that our greedy segmenter achieves improved results over previous neural network-based word segmenters, and its performance is competitive with state-of-the-art methods, despite its simple feature set and the absence of external resources for training.
Ma and Hinrichs REF proposed an embedding matching approach to CWS, which took advantage of distributed representations.
Accurate Linear-Time Chinese Word Segmentation via Embedding Matching
ACL
Computer Science