KEYWORD DISTINGUISHING METHODS IN THE PROCESS OF SCIENTIFIC TEXTS RENDERING
DOI: 10.23951/1609-624X-2018-8-45-50
The article deals with the problem of adequate keyword distinguishing from scientific texts in order to retain the meaning of the text and to lessen the risk of misinterpretation of information rendered by it. There is a great need for the effective methods of automatic keyword extraction due to the excessiveness of the data. The article presents the review of up-to-date approaches to text rendering and keyword distinguishing in foreign and Russian research. This research is essential for further investigation of the problem as it shows the advantages and disadvantages of the approaches found. The existing methods are subdivided into four groups: statistical approaches, natural language processing, machine training methods, hybrid methods combining the above mentioned three groups. The second part of the article presents a hybrid method for extracting keywords from scientific texts in the process of rendering law texts. It employs statistical and empiric approaches at its beginning taking into consideration the formal features of keywords (frequency, information weight, position). The next stages include linguistic methods of lexico-semantic analysis, building of word chains, the part-of-speech analysis. The final stage of extracting the categorical component of the meaning of the keywords is essential for describing the concept of the text and allows to restore the meaning of the whole text out of the few keywords. The research presents a list of categorical components of keywords extracted from abstracts of scientific texts of law discourse, which is the result of the application of the suggested method.
Keywords: rendering, scientific discourse, categorical component of the word meaning, text comprehension
References:
1. Mol’ А. Sotsiodinamika kul’tury [Sociodynamics of culture]. Мoscow, KomKniga Publ., 2005. 416 p. (in Russian).
2. Bharti Santosh Kumar, Babu Korra Sathya, Pradhan Anima. Automatic Keyword Extraction for Text Summarizationin Multi-document e-Newspapers Articles. European Journal of Advances in Engineering and Technology, 2017, vol. 4, issue 6, pp. 410–427. URL: http://www.ejaet.com/PDF/4-6/EJAET-4-6-410-427.pdf (accessed 15 August 2018).
3. Haggag Mohamed H. Keyword Extraction using Semantic Analysis. International Journal of Computer Applications (0975–8887), January 2013, vol. 61, no.1. URL: https://pdfs.semanticscholar.org/1b09/c3dc05e4c19c44969a06c5509123ad2f5158.pdf (accessed 13 August 2018).
4. Hulth Anette. Improved Automatic Keyword Extraction Given More Linguistic Knowledge. EMNLP ‘03 Proceedings of the 2003 conference on Empirical methods in natural language processing, Association for Computational Linguistics, 2003. pp. 216–223. URL: http://www.aclweb.org/anthology/W03-1028 (accessed 12 August 2018).
5. Kostomarov V. G., Burvikova N. D. Pretsedentnyy tekst kak redutsirovannyy diskurs [Precedential text as a kind of reduced discourse]. Yazyk kak tvorchestvo. K 70-letiyu V. P. Grigor’yeva: sbornik nauchnykh trudov [Language as creativity. To the 70th anniversary of V. P. Grigoryev: compilation of research-based articles], Moscow, Institute of the Russian Language Russian Academy of Sciences Publ., 1996, pp. 297–330 (in Russian).
6. Yatsko V. A. Simmetrichnoye referirovaniye: teoreticheskiye osnovy i metodika [Symmetrical rendering: theoretical bases and technique]. NTI, 2002, series 2, no. 5, pp.18–27 (in Russian).
7. Blyumenau D. I. Informatsionnyy analiz/sintez dlya formirovaniya vtorichnogo potoka dokumentov [Informational analysis/synthesis for the formation of the secondary influx of documents]. Saint-Petersburg, Professiya Publ., 2002. 235p. (in Russian).
8. Razinkina N. M. Funktsional’naya stilistika (na materiale angliyskogo i russkogo yazykov): ucheb. posobiye [Functional stylistics (on the material of the English and the Russian languages): textbook]. Moscow, Vysshaya shkola Publ., 2004. 271p. (in Russian).
9. Ercan G., Cicekli. I. Using lexical chains for keyword extraction. Information Processing & Management, 2007, vol. 43 (6), pp. 1705–1714. URL: https://www.sciencedirect.com/science/article/abs/pii/S0306457307000398 (accessed 19 August 2018).
10. Shekhtman N. A. Sistemnost’ leksiki i semantika slova: uchebnoye posobiye k spetskursu [The systematic issue of the vocabulary and semantics of the word: training manual]. Kuybyshev, Kuybyshev Publ., 1988. 84 p. (in Russian).
11. Church A. Vvedeniye v matematicheskuyu logiku [Introduction to the mathematical logics]. Moscow, Librokom Publ., 2009. Vol. 1. 482 p. (in Russian).
12. Gusserl’ E. Logicheskiye issledovaniya [Logics Research]. Saint-Petersburg, Akademicheskiy proekt Publ., 2011. Vol. 2. 576 p. (in Russian).
13. Longman Dictionary of Contemporary English. Harlow, Pearson Education Ltd., 2005. 1950 p.
( 635.86 kB ) 
( 415.4 kB )
Issue: 8, 2018
Series of issue: Issue 3
Rubric: COMPARATIVE LINGUISTICS AND LINGUISTICS OF THE TEXT
Pages: 45 — 50
Downloads: 1827