Word Sense Frequency Estimation for Russian: Verbs, Adjectives, and Different Dictionaries

P. 267–280.

Lopukhina A., Лопухин К. А.

In this paper, we investigate several extensions to our prior work on sense frequency estimation for Russian. Our method is based on semantic vectors and is able to achieve good accuracy for sense frequency estimation trained on dictionary entries from the Active Dictionary of Russian and unannotated corpora. We apply our method to verbs and adjectives to obtain sense frequencies for 329 verbs and 256 adjectives in an academic corpus and a web-based corpus. We compare frequency distributions against dictionary sense ordering and between two corpora and find that the first dictionary sense is not the most frequent for almost half of the words we studied. Evaluation of verbs and adjectives shows that frequency estimation error is lower than 15%. We investigate the effect of sense granularity, evaluating how the accuracy of our method changes when applied to more coarse-grained senses. We also investigate if our method can be applied to other dictionaries with less elaborate sense descriptions, by evaluating its accuracy when training on dictionary entries from two other dictionaries. 

Keywords: частотность, frequency, semantic vectors, w​ord sense frequency, word sense disambiguation (WSD), sense granularity

In book

Electronic lexicography in the 21st century. Proceedings of eLex 2017 conference

Brno: Lexical Computing CZ s.r.o., 2017.