Octet: Online Catalog Taxonomy Enrichment with Self-Supervision
Table 8. Ablation study of word embedding (W), head word semantics (H), lexical representation (L), and structural graph-based representation (G).
['[BOLD] Representation', '[BOLD] Dev Edge-F1', '[BOLD] Dev Ancestor-F1', '[BOLD] Test Edge-F1', '[BOLD] Test Ancestor-F1']
Feature Ablation As one can see, only using word embedding (W) does not suffice to identify hypernymy relations while adding the semantics of head words (H) explicitly boosts the performance significantly. Lexical (L) features are very effective and combining lexical representation with semantic representation (L + W + H) brings 17.2 absolute improvement in Edge-F1. The structural information is very useful in that even if we use word embedding (W) as the input of the structural representation (G), the Edge-F1 improves by 4.8x and the Ancestor-F1 improves by 59.3% upon W. The full model that incorporates various representations performs the best, indicating that they capture different aspects of the term pair relationship and are complementary to each other.