Word Segmentation on Micro-blog Texts with External Lexicon and Heterogeneous Data
Table 6: Results on CTB7-dev/test.
['[EMPTY]', 'on Dev Acc', 'on Dev P', 'on Dev R', 'on Dev F', 'on Test Acc', 'on Test P', 'on Test R', 'on Test F']
To investigate the performance on canonical texts of the models trained on CTB7 (and PD), we evaluate the models on CTB7-dev/test. We can get several reasonable yet interesting findings. First, comparing the results in all four major rows, we can see that using PD as extra labeled data consistently improves the F1 score by about 0.5%. Second, comparing the results in the first two major rows, it is clear that jointly modeling WS&POS outperforms the pure WS tagging model by about 0.3−0.5%. Third, comparing the results in the bottom two major rows, we can see that lexicon features are useful and improves F1 score by about 0.5%. Fourth, comparing the results in the first and third major rows, we can see that using WB as extra labeled data leads with the coupled approach to slight improvement in F1 score (0.03−0.24%). Third, looking into the third major row, we also get a few interesting findings: 1) using a joint WS&POS tagger to produce guide tags is better than using a WS tagger, indicating that jointly modeling WS&POS 2) PD is helpful by producing better guide tags, leading to higher F1 score on WB-dev by about 0.2%; 3) using both WS&POS tags for guide achieves nearly the same performance as using only WS tags.