Combined approach to problem of part-of-speech homonymy resolution in Russian texts

The Russian language has an inflective structure and does not have a strict word order. This causes processing difficulties, such as part-of-speech homonymy. This article is devoted to the mentioned issue. The existing approaches to resolving the morphological homonymy problem can be divided into the following groups: rule-based approaches, statistical approaches, machine learning approaches, and combined methods. In the paper, we showed that each approach has its advantages and disadvantages; however, combining several approaches can significantly increase the precision of the algorithm. Moreover, the article provides the analysis of the influence of certain features on the morphological homonymy resolution. The precision of the proposed algorithm is sufficient for its use in the tasks of intellectual text processing texts, for example, in machine translation and summarization systems. The proposed method is successfully used in the geographic location system. The main problem is the distinction between function words (conjunctions, particles, prepositions, interjections). Solving this problem is one of the priorities for the further work. We also plan to implement a system without a dictionary, in order to determine better morphological features for unknown words.