TY - GEN

T1 - Combined approach to problem of part-of-speech homonymy resolution in Russian texts

AU - Batura, Tatiana

AU - Bruches, Elena

PY - 2018/10/19

Y1 - 2018/10/19

N2 - 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.

AB - 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.

KW - Combined approach

KW - Homonymy resolution

KW - Machine learning

KW - Part-of-speech homonymy

KW - Text processing

UR - http://www.scopus.com/inward/record.url?scp=85057062280&partnerID=8YFLogxK

U2 - 10.1109/RUSAUTOCON.2018.8501718

DO - 10.1109/RUSAUTOCON.2018.8501718

M3 - Conference contribution

AN - SCOPUS:85057062280

BT - 2018 International Russian Automation Conference, RusAutoCon 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 International Russian Automation Conference, RusAutoCon 2018

Y2 - 9 September 2018 through 16 September 2018

ER -