Abstract
In recent years, high-precision machine learning models for traditional inflectional morphological analysis, as well as models for morpheme segmentation of words were built for Russian. Two these morphological tasks are evidently related, and some NLP applications may require to perform both of them, so development and evaluation of combined morphological model is of research interest. Such a model is supposedly useful for processing texts in languages with rich morphology (e.g., Russian), in particular, for deriving meaning of new words rarely encountered in texts. The paper presents a neural model implementing both inflectional analysis of Russian word forms (with morphological disambiguation) and their segmentation into constituent morphs with their classification. To train the model, a relevant dataset was built, by morphemic labeling of SynTagRus corpus, and transfer learning techniques were applied. Experimental evaluation of the model has shown its sufficiently high quality: 94.2% of precision for morphological tags disambiguation and 88–91% of word-level classification accuracy for segmentation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
Further experiments have shown performance gain about 20%.
- 5.
- 6.
- 7.
- 8.
References
Anastasyev, D.G.: Exploring pretrained models for joint morpho-syntactic parsing of Russian. In: Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2020”, Moscow, pp. 1–12 (2020)
Botha, J., Blunsom, P.: Compositional morphology for word representations and language modelling. In: International Conference on Machine Learning, pp. 1899–1907 (2014)
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)
Bolshakova, E., Sapin, A.: Comparing models of morpheme analysis for Russian words based on machine learning: In: Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2019”, Moscow, pp. 104–113 (2019)
Bolshakova, E., Sapin, A.: Bi-LSTM model for morpheme segmentation of Russian words. In: Ustalov, D., Filchenkov, A., Pivovarova, L. (eds.) AINL 2019. CCIS, vol. 1119, pp. 151–160. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-34518-1_11
Bolshakova, E., Sapin, A.: Building dataset and morpheme segmentation model for Russian word forms. In: Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2021”, Moscow, pp. 154–161 (2021)
Devlin, J., et al.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the NAACL: Human Language Technologies, vol. 1, pp. 4171–4186. ACL (2019)
Chollet, F.: Keras: Deep learning library for Theano and TensorFlow. https://keras.io/. Accessed 17 Nov 2021
Cotterell, R., Schutze, H.: Morphological word-embeddings. In: Proceedings of the 2015 Conference of the NAACL: Human Language Technologies, pp. 1287–1292. ACL (2015)
Creutz, M., Lagus, K.: Unsupervised models for morpheme segmentation and morphology learning. ACM Trans. Speech Lang. Process. 4(1), Article no. 3 (2007)
Harris, Z.S.: Morpheme boundaries within words: report on a computer test. Transformations Discourse Anal. Pap. 73, 68–77 (1967)
Hofmann, V., Pierrehumbert, J.B., Schutze, H.: Superbizarre is not superb: improving BERT’s interpretations of complex words with derivational morphology. In: Proceedings of the 59th Annual Meeting of the ACL, pp. 3594–3608 (2021)
Kanerva, J., et al.: Turku neural parser pipeline: an end-to-end system for the CoNLL 2018 shared task. In: Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pp. 133–142 (2018)
Korobov, M.: Morphological analyzer and generator for Russian and Ukrainian languages. In: Khachay, M.Y., Konstantinova, N., Panchenko, A., Ignatov, D.I., Labunets, V.G. (eds.) AIST 2015. CCIS, vol. 542, pp. 320–332. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26123-2_31
Lango, M., Žabokrtský, Z., Ševčíková, M.: Semi-automatic construction of word-formation networks. In: Proceedings of the 11th International Conference on Language Resources & Evaluation (2018)
Lyashevskaya, O.N., et al.: GRAMEVAL 2020 shared task: Russian full morphology and universal dependencies parsing. In: Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2020”, Moscow, pp. 553–569 (2020)
Peters, M.E., et al.: Deep contextualized word representations. In: Proceedings of North American Association for Computational Linguistics (NAACL), pp. 2227–2237 (2018)
Segalovich, I.: A fast morphological algorithm with unknown word guessing induced by a dictionary for a web search engine. In: MLMTA 2003, pp. 273–280. CSREA Press (2003)
Schmid, H.: Probabilistic part-of-speech tagging using decision trees. In: Proceedings of the International Conference on New Methods in Language Processing, pp. 44–49 (1994)
Smit, P., Virpioja S., Gronroos S., Kurimo M.: Morfessor 2.0: toolkit for statistical morphological segmentation. In: Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the ACL, Gothenburg, pp. 21–24 (2014)
Sorokin, A., et al.: MorphoRuEval-2017: an evaluation track for the automatic morphological analysis methods for Russian. In: Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2017”, Moscow, vol. 1, pp. 297–314 (2017)
Sorokin, A., Kravtsova, A.: Deep convolutional networks for supervised morpheme segmentation of Russian language. In: Ustalov, D., Filchenkov, A., Pivovarova, L., Žižka, J. (eds.) AINL 2018. CCIS, vol. 930, pp. 3–10. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01204-5_1
Sorokin, A., Smurov, I., Kirianov, P.: Tagging and parsing of multidomain collections. In: Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2020”, Moscow, pp. 670–683 (2020)
Straka, M., Straková, J., Hajic, J.: Prague at EPE 2017: the UDPipe system. In: Proceedings of the 2017 Shared Task on Extrinsic Parser Evaluation at the Fourth International Conference on Dependency Linguistics and the 15th International Conference on Parsing Technologies, pp. 65–74 (2017)
Tikhonov, A.N.: Word Formation Dictionary of Russian Language. Russkiy yazyk Publication, Moscow (1990)
Acknowledgements
Thе research has been partially supported by the Interdisciplinary Scientific and Educational School of Lomonosov Moscow State University «Brain, Cognitive Systems, Artificial Intelligence».
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bolshakova, E.I., Sapin, A.S. (2022). Building a Combined Morphological Model for Russian Word Forms. In: Burnaev, E., et al. Analysis of Images, Social Networks and Texts. AIST 2021. Lecture Notes in Computer Science, vol 13217. Springer, Cham. https://doi.org/10.1007/978-3-031-16500-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-16500-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16499-6
Online ISBN: 978-3-031-16500-9
eBook Packages: Computer ScienceComputer Science (R0)