Abstract
The paper deals with the task of automated terminology extraction. A two-stage technology for its solution is proposed, based on topic modeling and analyzing the context of the use of lexical units. The results of experimental verification of the technology and the prospects for its further development are presented.
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References
Korsun, I.A., Pal'chunov, D.E.: Teoretiko-model'nye metody izvlecheniya znanij o smysle ponyatij iz tekstov estestvennogo yazyka. Vestnik Novo-sibirskogo gosudarstvennogo universiteta. Seriya Informacionnye tekhnologii 14, 34–48 (2016)
Frantzi, K.T., Ananiadou, S., Tsujii, J.: The C-value/NC-value method of automatic recognition for multi-word terms. In: Nikolaou, C., Stephanidis, C. (eds.) ECDL 1998. LNCS, vol. 1513, pp. 585–604. Springer, Heidelberg. https://doi.org/10.1007/3-540-49653-X_35
Kageura, K., Umino, B.: Methods of automatic term recognition: a review. Terminology 3, 259–289 (1996)
Pazienza, M.T., Pennacchiotti, M., Zanzotto, F.M.: Terminology extraction: an analysis of linguistic and statistical approaches. In: Sirmakessis, S. (ed.) Knowledge Mining. Studies in Fuzziness and Soft Computing, vol. 185, pp. 255–279. Springer, Heidelberg (2005). https://doi.org/10.1007/3-540-32394-5_20
Astrakhantsev, N.A., Fedorenko, D.G., Turdakov, D.: Methods for automatic term recognition in domain-specific text collections: a survey. Program. Comput. Softw. 41, 336–349 (2015). https://doi.org/10.1134/S036176881506002X
Shao, W., Hua, B., Ma, Q., Liu, J., He, H., Chen, K.: A unsupervised method for terminology extraction from scientific text. In: EEKE@JCDL (2020)
Weiss, D., Petrov, S.: An Upgrade to SyntaxNet, New Models and a Parsing Competition (2017). http://ai.googleblog.com/2017/03/an-upgrade-to-syntaxnet-new-models-and.html
Mahasoeva, O.G., Pal'chunov, D.E.: Avtomatizirovannye metody postroeniya atomarnoj diagrammy modeli po tekstu estestvennogo yazyka. Vestnik Novosibirskogo gosudarstvennogo universiteta. Seriya Informacionnye tekhnologii 12, 64–73 (2014)
Jurafsky, D., Martin, J.: Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition (2008)
Palmer, M., Kingsbury, P., Gildea, D.: The proposition bank: an annotated corpus of semantic roles. Comput. Linguist. 31, 71–106 (2005). https://doi.org/10.1162/0891201053630264
About FrameNet | fndrupal. https://framenet.icsi.berkeley.edu/fndrupal/about
Boas, H.C.: From theory to practice: frame semantics and the design of FrameNet. In: Langer, S., Schnorbusch, D. (eds.) Semantisches Wissen im Lexikon. Tübingen, Narr (2005)
Hatty, A., Schlechtweg, D., Dorna, M., Schulte im Walde, S.: Predicting degrees of technicality in automatic terminology extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 2883–2889. Association for Computational Linguistics (2020)
Belaya T.I., Pasechnik P.A.: Vydelenie klyuchevyh ponyatij v tekstovom so-derzhimom s ispol'zovaniem statisticheskoj ocenki. Sovremennye problemy nauki i obrazovaniya (nauchnyj zhurnal) (2014)
Angelov, D.: Top2Vec: distributed representations of topics. arXiv:2008.09470 [cs, stat] (2020)
Honnibal, M., Montani, I., Van Landeghem, S., Boyd, A.: SpaCy: Industrial-Strength Natural Language Processing in Python. Zenodo (2020)
Acknowledgements
The reported study was funded by RFBR, project number 20–07-00754 A.
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Shishaev, M.G., Dikovitsky, V.V., Lomov, P.A. (2021). Concept and Preliminary Testing of the Two-Stage Technology of Terminology Extraction on the Basis of Topic Modeling and Context Analysis. In: Silhavy, R. (eds) Informatics and Cybernetics in Intelligent Systems. CSOC 2021. Lecture Notes in Networks and Systems, vol 228. Springer, Cham. https://doi.org/10.1007/978-3-030-77448-6_62
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