ISSN 2071-8594

Russian academy of sciences


Gennady Osipov

D. Devyatkin, Yu. Kuznetcova, N. Chudova Methods for mental operations detection in scientific publications


The paper presents results of a study of methods for a mental structure detection in scientific text. Initially, we consider the concepts of mental operations and mental structure of a scientific text. The reasons for choosing the taxonomy for mental operations used in our approach are also presented. We set the problem of mental operations detection in texts and propose the methods for solving it. An empirical study on an annotated dataset and on a corpus of more than 7k scientific articles showed the applicability of the proposed methods for detection a mental structure of a scientific text. Informative features of mental structures of texts from different research areas were also detected.


scientific text, mental operations, representation for mental operations, mental structure of a scientific text, relational-situational analysis.

PP. 36-46.


1. Filosofiya [Philosophy]/ Ed. Yu.Kharin. Minsk.TetraSystems. 2000.
2. Kislov B. O specifike nauchnogo metoda [Specifics of the scientific method]// ISEA News. 2004. № 3. p.86-89.
3. Shnyakina N. Situaciya poznaniya zapaha v yazykovyh strukturah (subekt-obekt poznavatelnoe dejstvie) [The situation of recognition of the smell in language structures] (subject-object-cognitive action) // ISLU Bulletin. 2013. №4 (25). p.121-128.
4. Galich G. Kognitivnye strategii i yazykovye struktury [Cognitive strategies and language structures]. Omsk, 2011.
5. Chervonny A. Opyt rekonstrukcii frazeosemanticheskogo polya mentalnyh dejstvij cheloveka na materiale francuzskogo yazyka [Experience in reconstructing the phraseosemantic field of mental actions of a person on the material of the French language] // Bulletin of Taganrog institute after A. Chekhov. 2016(2) p.187-193.
6. Bazhenova E. Stilistiko-rechevaya organizaciya nauchnogo teksta [Stylistic and verbal organization of scientific text] // Beograd, 2003(2). p. 129–141.
7. Bazhenova E. Pragmaticheskie edinicy nauchnogo teksta [Pragmatic units of scientific text] //
8. Kotyurova M.P., Bazhenova E. A. Kultura nauchnoj rechi. Tekst i ego redaktirovanie [Culture of scientific speech. Text and editing], M. Flinta. 2008.
9. Salimovskij V.A. Zhanry rechi v funkcionalno-stilisticheskom osveshhenii russkij akademicheskij tekst [Speech genres in functional-stylistic coverage of Russian academic text]. Diss dok filol nauk [Dr. Sci theses]. Ekaterinburg. 2002.
10. Teufel S., Carletta J., Moens M. An annotation scheme for discourse-level argumentation in research articles //Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics. – Association for Computational Linguistics, 1999. – P. 110-117.
11. Kirschner C., Eckle-Kohler J., Gurevych I. Linking the thoughts: Analysis of argumentation structures in scientific publications //Proceedings of the 2nd Workshop on Argumentation Mining. – 2015. – p. 1-11.
12. Liakata M. et al. Corpora for the Conceptualisation and Zoning of Scientific Papers //LREC. – 2010.
13. Guo Y., Reichart R., Korhonen A. Improved Information Structure Analysis of Scientific Documents Through Discourse and Lexical Constraints //HLT-NAACL. – 2013. – С. 928-937.
14. Liakata M. et al. Automatic recognition of conceptualization zones in scientific articles and two life science applications //Bioinformatics. – 2012. – Vol. 28. – No. 7. – P. 991-1000.
15. Liu H. Automatic Argumentative-Zoning Using Word2vec //arXiv preprint arXiv:1703.10152. – 2017.
16. Osipov G. et al. Relational-situational method for intelligent search and analysis of scientific publications //Proceedings of the Integrating IR Technologies for Professional Search Workshop. – 2013. – p. 57-64.
17. P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, “Enriching Word Vectors with Subword Information”,
arXiv:1607.04606, 2016, [unpublished].
18. G. Osipov, D. Devyatkin, Yu. Kuznetcova, A. Shvets Vozmozhnosti intellektualnogo analiza nauchnyh tekstov na osnove postroeniya kognitivnoj modeli nauchnogo teksta [possibilities of intellectual analysis of scientific texts by construction of their cognitive models] // Artificial Intelligence and Decision Making [in print].
19. Chung J. et al. Empirical evaluation of gated recurrent neural networks on sequence modeling //arXiv preprint arXiv:1412.3555. – 2014.
20. Bridle J. S. Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition //Neurocomputing. – Springer Berlin Heidelberg, 1990. – p. 227-236.
21. Zhou Z. H. Ensemble methods: foundations and algorithms. – CRC press, 2012.
22. Okazaki N. CRFsuite: a fast implementation of conditional random fields (CRFs). – 2007.
23. Breiman L. Random forests //Machine learning. – 2001. – Vol. 45. – №. 1. – p. 5-32.
24. Pedregosa F. et al. Scikit-learn: Machine learning in Python //Journal of Machine Learning Research. – 2011. – Vol. 12. – No. Oct. – p. 2825-2830.
25. Kohavi R. et al. A study of cross-validation and bootstrap for accuracy estimation and model selection //Ijcai. – 1995. – Vol. 14(2). – p. 1137-1145.
26. Flach P. Machine learning: the art and science of algorithms that make sense of data. – Cambridge University Press. 2012.