Multiple Character Embeddings for Chinese Word Segmentation
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Chinese word segmentation (CWS) is often regarded as a character-based sequence labeling task in most current works which have achieved great performance by leveraging powerful neural networks. However, these works neglect an important clue: Chinese characters contain both semantic and phonetic meanings. In this paper, we introduce multiple character embeddings including Pinyin Romanization and Wubi Input, both of which are easily accessible and effective in depicting semantics of characters. To fully leverage them, we propose a novel shared Bi-LSTM-CRF model, which fuses multiple features efficiently. Extensive experiments on five corpora demonstrate that extra embeddings help obtain a significant improvement.
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