WordRank: Learning Word Embeddings via Robust Ranking
Table 3: Performance of the best word2vec, GloVe and WordRank models, learned from 7.2 billion tokens, on six similarity tasks and Google semantic and syntactic subtasks.
['Model', 'Word Similarity WordSim', 'Word Similarity WordSim', 'Word Similarity Bruni et', 'Word Similarity Radinsky', 'Word Similarity Luong et', 'Word Similarity Hill et al.', 'Word Analogy Goog', 'Word Analogy Goog']
To further evaluate the model performance on the word similarity/analogy tasks, we use the best performing models trained on the 7.2-billion-token corpus to predict on the six word similarity datasets described in Sec. Moreover, we breakdown the performance of the models on the Google word analogy dataset into the semantic and syntactic subtasks. As can be seen, WordRank outperforms word2vec and GloVe on 5 of 6 similarity tasks, and 1 of 2 Google analogy subtasks.