Investigating Self-Attention Network for Chinese Word Segmentation
Table 6: Segmentation precision of noun entities with the highest frequency.
['[BOLD] Word', '[BOLD] Count', '[BOLD] M1', '[BOLD] M2', '[BOLD] M3']
Noun Entity Segmentation Noun entities raise a key problem for cross-domain CWS. M1 and M2 represent “L-SAN+CRF+BERT” and “L-SAN+CRF+BERT+t”, respectively, while M3 represents “L-SAN+CRF+BERT+t_b”. As the table shows, the average precision of MI is 0.55. By using neural type-supervised domain adaptation method, the average precision of M2 has a improvement of 0.18 in absolute value.