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In this paper, we present a distributional word embedding model trained onone of the largest available Russian corpora: Araneum Russicum Maximum (over 10billion words crawled from the web). We compare this model to the model trainedon the Russian National Corpus (RNC). The two corpora are much different intheir size and compilation procedures. We test these differences by evaluatingthe trained models against the Russian part of the Multilingual SimLex999semantic similarity dataset. We detect and describe numerous issues in thisdataset and publish a new corrected version. Aside from the already known factthat the RNC is generally a better training corpus than web corpora, weenumerate and explain fine differences in how the models process semanticsimilarity task, what parts of the evaluation set are difficult for particularmodels and why. Additionally, the learning curves for both models aredescribed, showing that the RNC is generally more robust as training materialfor this task.

Size vs. Structure in Training Corpora for Word Embedding Models: Araneum Russicum Maximum and Russian National Corpus