Исследовательская группа

Лаборатория языковых инструментов

Публикации

  • Sergey Bozhko, Leyla Khatbullina, Semyon Grigorev

    The Bar-Hillel theorem states that context-free languages are closed under intersection with a regular set. This theorem has a constructive proof and thus provides a formal justification of correctness of the algorithms for applications mentioned above. Mechanization of the Bar-Hillel theorem, therefore, is both a fundamental result of formal language theory and a basis for the certified implementation of the algorithms for applications. In this work, we present the mechanized proof of the Bar-Hillel theorem in Coq.

    Logic, Language, Information, and Computation,
  • Nikita Mishin, Iaroslav Sokolov, Egor Spirin, Vladimir Kutuev, Egor Nemchinov, Sergey Gorbatyuk, and Semyon Grigorev

    Recently proposed matrix multiplication based algorithm for context-free path querying (CFPQ) offloads the most performance-critical parts onto boolean matrices multiplication. Thus, it is possible to achieve high performance of CFPQ by means of modern parallel hardware and software. In this paper, we provide results of empirical performance comparison of different implementations of this algorithm on both real-world data and synthetic data for the worst cases.

    Proceedings of the 2nd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA),
  • Semyon Grigorev and Polina Lunina

    We propose a way to combine formal grammars and artificial neural networks for biological sequences processing. Formal grammars encode the secondary structure of the sequence and neural networks deal with mutations and noise. In contrast to the classical way, when probabilistic grammars are used for secondary structure modeling, we propose to use arbitrary (not probabilistic) grammars which simplifies grammar creation. Instead of modeling the structure of the whole sequence, we create a grammar which only describes features of the secondary structure. Then we use matrix-based parsing to extract features: the fact that some substring can be derived from some nonterminal is a feature. After that, we use a dense neural network to process features.

    Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOINFORMATICS,
  • 46th ACM SIGPLAN Symposium on Principles of Programming Languages (POPL 2019). Lisbon, Portugal,
  • N. Mishin, A. Fefelov, V. Bushev, I. Kirilenko, and D. Berezun
    Survey on Blockchain Technology, Consensus Algorithms, and Alternative Distributed Technologies
    SEIM-19,
  • Kirill Smirenko, Semyon Grigorev

    Extended abstract at TyDe 2018 (at ICFP).

  • D.V. Luciv, D.V. Koznov, G.A. Chernishev, A.N. Terekhov, K.Y. Romanovsky, D.A. Grigoriev
    Programming and Computer Software,
  • Ekaterina Verbitskaia, Ilya Kirillov, Ilya Nozkin, Semyon Grigorev

    Transparent integration of a domain-specific language for specification of context-free path queries (CFPQs) into a general-purpose programming language as well as static checking of errors in queries may greatly simplify the development of applications using CFPQs. LINQ and ORM can be used for the integration, but they have issues with flexibility: query decomposition and reusing of subqueries are a challenge. Adaptation of parser combinators technique for paths querying may solve these problems. Conventional parser combinators process linear input, and only the Trails library is known to apply this technique for path querying. We demonstrate that it is possible to create general parser combinators for CFPQ which support arbitrary context-free grammars and arbitrary input graphs. We implement a library of such parser combinators and show that it is applicable for realistic tasks.

    Proceedings of the 9th ACM SIGPLAN International Symposium on Scala,
  • E. Moiseenko, A. Podkopaev
    NTV SPbSTU,
  • Rustam Azimov, Semyon Grigorev
    GRADES-NDA '18 Proceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA),

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