Integrating Multiplicative Featuresinto Supervised Distributional Methods for Lexical Entailment
Table 4: Test performance (F1) on BLESS in the RAND and OOD setups, using Glove and Word2vec.
['[BOLD] Vector/  [BOLD] Classifier', '[BOLD] Vector/  [BOLD] Classifier', '[BOLD] RAND → [ITALIC] vy', '[BOLD] RAND  [BOLD] Diff', '[BOLD] RAND  [BOLD] Diff ⊕  [BOLD] Mult', '[BOLD] RAND  [BOLD] Sum', '[BOLD] RAND  [BOLD] Sum ⊕  [BOLD] Mult', '[BOLD] RAND  [BOLD] Concat', '[BOLD] RAND  [BOLD] Concat ⊕  [BOLD] Mult', '[BOLD] OOD → [ITALIC] vy', '[BOLD] OOD  [BOLD] Diff', '[BOLD] OOD  [BOLD] Diff ⊕  [BOLD] Mult', '[BOLD] OOD  [BOLD] Sum', '[BOLD] OOD  [BOLD] Sum ⊕  [BOLD] Mult', '[BOLD] OOD  [BOLD] Concat', '[BOLD] OOD  [BOLD] Concat ⊕  [BOLD] Mult']
Concat ⊕ Mult performed consistently well, intuitively because Concat captures the typicality of each word in the relation (e.g., if y is a typical hypernym) and Mult captures the similarity between the words (where Concat alone may suggest that animal is a hypernym of apple). To take a closer look at the gain from adding