Retrofitting Distributional Embeddings to Knowledge Graphswith Functional Relations
Table 2: Retrofitting to WordNet. Reported values are Spearman correlations for the word similarity tasks and mean cosine similarity for the syntatic relation task. These are deterministic evaluations, so the only source of stochasticity is the optimization of the FR-Neural model.
['[BOLD] Retrofitting Model', '[BOLD] Word Similarity WordSim-353', '[BOLD] Word Similarity MTurk-771', '[BOLD] Word Similarity MTurk-287', '[BOLD] Syntactic Relation Google Analogy']
Here, we evaluate standard lexical metrics for word embeddings: word similarity and syntatic relations. For word similiarity tasks, the evaluation metric is the Spearman correlation between predicted and annotated similarities; for syntatic relation, the evaluation metric is the mean cosine similarity between the learned representation of the correct answer and the prediction by the vector offset method In contrast to our other experiments, here the only stochastic behavior is due to stochastic gradient descent training, not sampling of evaluation samples. Even though the WordNet knowledge graph largely satisfies the assumptions of the naïve retrofitting model, the flexible FR framework achieves sustained improvements for both word similarity datasets (WordSim-353; \newcitefinkelstein2001placing,