Geometry of Compositionality
Table 3: Experiments on ENC, EVPC and GNC Datasets.
['Dataset', 'Method', 'First Component Precision (%)', 'First Component Recall (%)', 'First Component F1 score (%)', 'Second Component Precision (%)', 'Second Component Recall (%)', 'Second Component F1 score (%)']
Detection Results: Our subspace representation (SubSpace) uses CBOW and MSSG embeddings, and we use both average and PCA approximations as context embeddings. Their performance is shown in the row of “SubSpace (CBOW)” and “SubSpace (MSSG)” respectively. We have two baseline methods: (1) pointwise mutual information (PMI). PMI=logP(w1w2)P(w1)P(w2), Higher PMI indicates the phrase is more likely to be non-compositional. (2) Average sentence embedding method. and we measure compositionality by the cosine similarity between the target word vector and the sentence vector. The corresponding performance is reported in the rows of “Avg Cxt (CBOW)” and “Avg Cxt (MSSG)”. specifically their methods based on word definitions, synonyms and idiom tags (denoted by ALLDEFS+SYN, ITAG+SYN, ALLDEFS) provided by wikitionary.