Radical-Enhanced Chinese Character Embedding
Table 2: Comparison of F1 on the test set of CTB5 and CTB7.
['Method', 'CTB5  [ITALIC] P', 'CTB5  [ITALIC] R', 'CTB5  [ITALIC] F1', 'CTB7  [ITALIC] P', 'CTB7  [ITALIC] R', 'CTB7  [ITALIC] F1']
We compare our radical-enhanced model (alpha=0.8 tuned on the development set) with C&W model and Word2Vec in the framework of Neural CRF. One reason is that the radial-enhanced model is capable to capture the semantic connections between characters with the same radical, which usually have similar semantic meaning and grammatical usage yet not explicitly modeled in C&W and word2vec. Another reason is that, the embeddings of lower-frequent characters are typically not well estimated by C&W and word2vec due to the lack of syntactic contexts. In the radical-enhanced model, their radicals bring important semantic information thus we obtain better embedding results. We also compare with two CRF-based baseline methods. CRF(character) is the use of linear-chain CRF with character as its feature. In CRF(character+radical), we utilize the radical information and the character as features with linear-chain CRF. Results of CRF(character) and CRF(character+radical) show that simply using radical as feature does not obtain significant improvement. Our radical-enhanced method outperforms two CRF-based baselines on both datasets, which further verifies the effectiveness of our method.