Modelling Semantic Categories using Conceptual Neighborhood
Table 2: Results (%) of the category induction experiments
['[EMPTY]', '[BOLD] Pr', '[BOLD] Rec', '[BOLD] F1']
In this table, we show results for different choices of the number of selected conceptual neighbors k, ranging from 1 to 5. As can be seen from the table, our approach substantially outperforms all baselines, with Multi being the most competitive baseline. Interestingly, for the Similarity baseline, the higher the number of neighbors, the more the performance approaches that of Multi. The relatively strong performance of Multi shows that using the siblings of a category in the BabelNet taxonomy is in general useful. However, as our results show, better results can be obtained by focusing on the predicted conceptual neighbors only. It is interesting to see that even selecting a single conceptual neighbor is already sufficient to substantially outperform the Gaussian model, although the best results are obtained for k=4. Comparing the WEA and BERT variants, it is notable that BERT is more successful at selecting the single best conceptual neighbor (reflected in an F1 score of 47.0 compared to 41.9). However, for k≥2, the results of the WEA and BERT are largely comparable.