Learning Topics using Semantic Locality
TABLE II: fixed total feature number word/word pair performance evaluation
['[BOLD] mAP', '[BOLD] F = 10.5K word', '[BOLD] F = 10.5K word pair', '[BOLD] F = 11K word', '[BOLD] F = 11K word pair', '[BOLD] F = 11.5K word', '[BOLD] F = 11.5K word pair', '[BOLD] F = 12K word', '[BOLD] F = 12K word pair', '[BOLD] F = 12.5K word', '[BOLD] F = 12.5K word pair', '[BOLD] F = 15K word', '[BOLD] F = 15K word pair']
the word/word pair combined model almost always performs better than the word-only model. For the mAP1, the mAP5 and the mAP10, the most significant improvement occurs when total feature size is set to = 11000. About 10.48%, 7.97%, and 9.83% improved were found compared to the word-only model. For the mAP3, the most significant improvement occurs when the total feature size is set to = 12000, and about 9.35% improvement is achieved by considering word pair. Again, the word/word pair combined model outperforms the word-only model almost all the time. For the mAP1,3,5 and 10 up to 1.05%, 1.11%, 1.02% and 0.89% improvement are achieved. Similar to previous two datasets, all the results from word/word pair combined model are better than the word-only model. For the mAP1,3,5 and 10, the most significant improvement occurs when when the total feature size is set to = 11500. Up to 10.40%, 11.91%, 12.46% and 12.99% improvements can achieved.