Exploring phrase-compositionality in skip-gram models
Table 1: Spearman’s correlation using different embeddings
['[EMPTY]', '[ITALIC] ρ']
We use the senna toolkit to extract the POS tag labels for each sentence and combine adjacent noun-verb pairs. As the evaluation dataset is lemmatized, we also lemmatize the Wikipedia corpus to avoid sparsity. We evaluate the cosine similarity between the composed reference subject intransitive verb pair and its landmarks. Then we compute the Spearman’s correlation between the similarity scores and the human ratings. We can see that the compositions of subject and intransitive verbs can be learned using the compositional model and the best performance is achieved using the joint model. We use α=1 and varying α does not change much of the performance.