Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion
Table 3. Mean Average Precision across all queries on Wiki and APR.
['Methods', '[ITALIC] Wiki MAP@10', '[ITALIC] Wiki MAP@20', '[ITALIC] Wiki MAP@50', '[ITALIC] APR MAP@10', '[ITALIC] APR MAP@20', '[ITALIC] APR MAP@50']
The result clearly shows that our model has the best performance over all the methods in both datasets. Among all the baseline methods, BERT is the strongest one, since it stores a very large pre-trained language model to represent word semantics in a piece of context, which also explains why we have a very large margin over previous iterative-based methods like SetExpan when we incorporate BERT as embedding-based features. However, BERT itself could not outperform our model, which implies that modeling distributional similarity alone is not enough for generating accurate expansion. We can also observe that Set-CoExpan also outperforms the ablations without auxiliary sets or flexgram, which justifies that auxiliary sets indeed improve the set expansion process by providing clearer discriminative context features for multiple sets to prevent them from expanding into each other’s semantic territory. The advantage of Set-CoExpan over the ablation without flexgram also proves that it is necessary to cope with the data sparsity problem by extracting independent units from a fixed local context window. In this way, we relieve the hard-matching constraint brought by skip-gram features by making it more flexible. What’s more, Set-CoExpan has a larger advantage over all the baselines when the ranking list is longer, which indicates that when the seed set gradually grows out of control and more noises appear, our method is able to steer the direction of expansion and to set barriers for out-of-category words to come in.