Automatic Morphemic Analysis of Russian Words

Ch. 9. P. 85–94.

Мальтина Л. П., Malafeev A.

The paper considers the task of the morphemic analysis of Russian words and compares the efficiency of several proposed models. These models can be divided into three groups: derivational and inflectional rule-based, proba- bilistic, and hybrid models. The latter achieved state-of-the-art results of 0.848 F-score on a test set of 500 Russian words. The models use dictionaries of morphs and words and information about the part of speech and other morphological fea- tures of the word. Importantly, our solution takes into account synchronic word- formative relations between words. This allows for analyzing words in any gram- matical form, as well as previously unseen words. Our system, which we make freely available to the community, also features morphemic annotation of entire texts and search for specified morphs.

Keywords: вероятностные модели, natural language processing, probabilistic models, морфемный анализ, автоматическая обработка текстов, morphemic analysis for Russian, derivational and inflectional rules, morphemic annotation of texts, search of morphs

In book

Supplementary Proceedings of the 7th International Conference on Analysis of Images, Social Networks and Texts (AIST-SUP 2018), Moscow, Russia, July 5-7, 2018

Aachen: CEUR Workshop Proceedings, 2018.