A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing
Table 3: Results of Chinese word segmentation.
['Models', 'Tag Set', 'CTB-5  [ITALIC] F1 [ITALIC] seg', 'CTB-5  [ITALIC] Pseg', 'CTB-5  [ITALIC] Rseg', 'CTB-7  [ITALIC] F1 [ITALIC] seg', 'CTB-7  [ITALIC] Pseg', 'CTB-7  [ITALIC] Rseg', 'CTB-9  [ITALIC] F1 [ITALIC] seg', 'CTB-9  [ITALIC] Pseg', 'CTB-9  [ITALIC] Rseg']
The first two rows present the difference between whether utilizing CRF on the top of MLP. CRF’s performance is slightly better than its counterpart. The first row and the third row display the comparison between different tag scenario, the {B,M,E,S} tag set is slightly better than the {app,seg} tag set. Jointly training Chinese word segmentation and dependency parsing achieves comparable or slightly better Chinese word segmentation than training Chinese word segmentation alone. Although head prediction is not directly related to Chinese word segmentation, the head character can only be the end of a word, therefore combination between Chinese word segmentation and character dependency parsing actually introduces more supervision for the former task. On CTB-5, the Joint-binary and Joint-multi models are slightly worse than the joint-SegOnly model. The reason may be that the CTB-5 dataset is relatively small and the complicated models suffer from the overfitting. We assume this is because the extra supervision from dependency parsing labels is informative for word segmentation.