Modeling Semantic Relatedness using Global Relation Vectors
Table 5: Results for the relation induction task using alternative word embedding models.
['[EMPTY]', 'GloVe Google', 'GloVe Google', 'GloVe DiffVec', 'GloVe DiffVec', 'SkipGram Google', 'SkipGram Google', 'SkipGram DiffVec', 'SkipGram DiffVec', 'CBOW Google', 'CBOW Google', 'CBOW DiffVec', 'CBOW DiffVec']
In particular, we show results for the standard GloVe model, SkipGram and the Continuous Bag of Words (CBOW) model. As can be observed, our variant leads to better results than the original GloVe model, both for the baselines and for our relation vectors. The difference is particularly noticeable for DiffVec. The difference is also larger for our relation vectors than for the baselines, which is expected as our method is based on the assumption that context word vectors can be interpreted in terms of PMI scores, which is only true for our variant. For SkipGram, we see a similar drop in performance on the DiffVec test set, but not for the baselines on the Google Analogy test set, which is perhaps not sufficiently challenging to see clear differences. Finally, the results for CBOW are similar to these for GloVe and SkipGram for the Google Analogy test set, but they are worse in the case of DiffVec. These results show that the proposed changes to the GloVe model indeed lead to a meaningful improvement. In the remainder of this paper, we will therefore only consider our variant.