Evaluation of Greek Word Embeddings
Table 3: Summary for 3CosAdd and top-1 nearest vectors.
['Category Semantic', 'Category no oov words', 'gr_def 58.42%', 'gr_neg10 59.33%', 'cc.el.300  [BOLD] 68.80%', 'wiki.el 27.20%', 'gr_cbow_def 31.76%', 'gr_d300_nosub 60.79%', 'gr_w2v_sg_n5 52.70%']
We compared these models in word analogy task. Due to space limitations, we show summarized results only for 3CosAdd in Table 3 and move the rest in supplementary material. Considering the two aggregated categories of syntactic and semantic word analogies respectively and both 3CosAdd and 3CosMul metrics, model cc.el.300 has outperformed all the other models apart from the case of the Syntactic category when we included the out-of-vocabulary (oov) terms [CONTINUE] where the model gr def had the best performance. Model cc.el.300 was the only one that was trained with CBOW and position-weights. Model wiki.el, trained only on Wikipedia, was the worst almost in every category (and sub-category). The five models that were trained on the large scale web content (Outsios et al., 2018) had lower percentage of oov terms in comparison with the other two. In some cases where oov terms were considered, they outperformed model cc.el.300 or had a better ranking considering the accuracy rate in most categories or sub-categories. In the basic categories, syntactic and semantic, model gr cbow def was the only one that performed much worse in the semantic category than in the syntactic one. All the other models did not have large differences in performance between semantic and syntactic categories.