The paper introduces two methods for semantic role labeling of Russian texts. The first method is based on semantic dictionary that contains information about predicates, roles and syntaxeme features that correspond to the roles. It also uses heuristics and integer linear programming to find the best joint assignment of roles. The second method is data-driven semantic-syntactic parsing, which was implemented using MaltParser. It performs transition-based data-driven parsing simultaneously building a syntactic tree and assigning semantic roles. It was trained with various feature sets on SynTagRus Treebank, which was automatically enriched with semantic roles by the dictionary-based parser. We managed to automatically alleviate mistakes in the training corpus using output of the datadriven parser. We evaluated the performance of the parsers on the subcorpus of SynTagRus, which we manually annotated with semantic information. The dictionary-based parser and the data-driven semantic-syntactic parser showed close performance. Although the data-driven parser did not outperform the dictionary-based parser, we expect that it can be beneficial in some cases and has potentials for further improvement.
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РИНЦ: https://elibrary.ru/item.asp?id=23969597
ResearchGate: https://www.researchgate.net/publication/288641007_Methods_for_semantic_role_labeling_of_Russian_texts
Shelmanov A. O., Smirnov I. V. Methods for Semantic Role Labeling of Russian Texts // Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference "Dialogue" (2014). Issue 13 (20). — 2014. — pp. 580-592.