Categorization in the Wild: Generalizing Cognitive Models to Naturalistic Data across Languages
Table 4: Model performance on the concept prediction task in terms of precision at rank 1, 10, 20, and average rank assigned. We compare BCF (this work), BayesCat, a co-occurrence baseline, Strudel (for English only), and a random baseline. Results are reported for English, German, French, Chinese and Arabic.
['[EMPTY]', 'model', 'pr@1', 'pr@10', 'pr@20', 'avg rank']
Both BCF and the co-occurrence model outperform the random baseline by a large margin, and BCF achieves consistently highest scores. Both Bayesian models (BCF and BayesCat) outperform the co-occurrence model across all metrics and conditions. We assume that plain concept-feature co-occurrence information might be too sparse to provide a strong signal of concept relevance given a set of features. The Bayesian models, on the other hand, learn complex correspondences between features and all concepts in a category. BayesCat and BCF perform comparably given that they exploit local co-occurrence relations in similar ways. BCF learns feature associations which discriminate concepts more accurately, suggesting that the joint learning objective and structured feature information is beneficial.