nach oben

Automatic Documentation and Mathematical Linguistics

Erschienen in:

01.10.2022 | AUTOMATED TEXT PROCESSING

Developing a Semantic Role Labeling System in Scientific and Technical Texts on Aviation and Astronautics

verfasst von: Ju. I. Butenko, M. L. Galetka, E. E. Sineva

Erschienen in: Automatic Documentation and Mathematical Linguistics | Ausgabe 5/2022

Einloggen, um Zugang zu erhalten

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The problem of automatic semantic analysis in natural language processing systems is considered. It is shown that annotating semantic roles is a promising method of analysis, which consists in the recognition of predicate-argument structures in each sentence. A program developed for marking semantic roles in Russian texts is described, and 2000 lexical units are marked on the examples of texts on aviation and astronautics. The unique inventory of semantic roles for aerospace domain is formed. Frame semantics method and ontological approach to frame organization in knowledge base are offered as a model of knowledge representation to organize labeled semantic roles.

Vorheriger Artikel Patterns of Using the Z-Score for Text Classification Purposes

Nächster Artikel The Phenomenon of Insight in the Light of Information Theory

Sidnyaev, N.I., Butenko, Iu.I., and Bolotova, E.E., Statistical and linguistic decision-making techniques based on fuzzy set theory, Advances in Intelligent Systems, Computer Science and Digital Economics. CSDEIS 2019, Hu, Z., Petoukhov, S., and He, M., Eds., Advances in Intelligent Systems and Computing, vol. 1127, Cham: Springer, 2020, pp. 165–174. https://doi.org/10.1007/978-3-030-39216-1_16

Sidnyaev, N.I., Butenko, Yu.I., and Sineva, E.E., Use of case grammar in information search in the expert system knowledge base on aircraft structures, Sist. Sredstva Inf., 2021, vol. 31, no. 3, pp. 80–87. https://doi.org/10.14357/08696527210307CrossRef

Lesnikov, S.V., The types of marking of text corpora of the Russian language, Nauchn.-Tekhn. Inform., Ser. 2. Protsessy Sist., 2019, no. 9, pp. 27–30. https://doi.org/10.36535/0548-0027-2019-09-4

Kuznetsov, I.O., Automatic selection of verb actants: Theoretical basis and modern approaches, Nauchn.-Tekhn. Inform., Ser. 2. Protsessy Sist., 2012, no. 12, pp. 34–44.

Fillmore, C.J. and Baker, C.F., Frame semantics for text understanding, Proc. WordNet and Other Lexical Resources Workshop, NAACL, 2001, vol. 6, pp. 20–32.

Apresyan, Yu.D., Leksicheskaya semantika (Lexical Semantics), Moscow: Vostochnaya Literatura Ross. Akad. Nauk, 1995, vol. 1.

Fillmore, Ch.J., The case for case, Universals in Linguistic Theory, Back, E. and Harms, R., Eds., New York: Harper and Row, 1968, pp. 1–88.

Bogdanov, V.V., Strukturno-semanticheskaya organizatsiya predlozheniya (Structural-Semantic Organization of a Sentence), Leningrad: Izd-vo Leningrad. Gos. Univ., 1977.

Susov, I.P., Deep aspects of sentence semantics, Problemy semantiki (Problems of Semantics), Moscow, 1974, pp. 58–65.

10.

Tregubov, A.S., Methods of intellectual analysis of text tonality to search for polar opinions, Perspektivy Nauki, 2020, no. 2, pp. 13–18.

11.

Naidenova, K.A. and Nevzorova, O.A., Machine learning for natural language processing: Contemporary state, Uch. Zap. Kazan. Univ. Ser.: Fiz.-Mat. Nauki, 2008, vol. 150, no. 4, pp. 5–24.

12.

Xue, N. and Palmer, M., Calibrating features for semantic role labeling, Empirical Methods in Natural Language Processing, 2004, pp. 88–94.

13.

Gildea, D. and Jurafsky, D., Automatic labeling of semantic roles, Comput. Linguist., 2020, vol. 28, no. 3, pp. 245–288. https://doi.org/10.1162/089120102760275983CrossRef

14.

Foland, W. and Martin, J.H., Dependency-based semantic role labeling using convolutional neural networks, Proc. Fourth Joint Conf. on Lexical and Computational Semantics, Denver, Colo., 2015, Association for Computational Linguistics, 2015, pp. 279–288. https://doi.org/10.18653/v1/S15-1033

15.

Ermakov, A.E. and Pleshko, V.V., Semantic interpretation in systems of computer language processing, Inf. Tekhnol., 2009, no. 6, pp. 2–7.

16.

Smirnov, I.V., Shelmanov, A.O., Kuznetsova, E.S., and Khramoin, I.V., Semantic-syntactic analysis of natural languages. Part II: Method of semantic-syntactic analysis of texts, Iskusstv. Intell. Prinyatie Reshenii, 2014, no. 1, pp. 11–24.

17.

Kashkin, E.V. and Lyashevskaya, O.N., Semantic roles and structural network in FrameBank system, Komp’yut. Linvist. Intell. Tekhnol., 2013, vol. 12, no. 19, pp. 325–343.

18.

Zakiev, M.Z., Tatarskaya grammatika (Tatar Grammar), vol. 3: Sintaksis (Syntax), Kazan: Tatknigoizdat, 1992.

19.

Boldyrev, N.N., Knowledge representation in the system of language, Vopr. Kognit. Lingvist., 2007, no. 4, pp. 17–27.

20.

Miller, G.A., Wordnet: A lexical database for English, Commun. ACM, 1995, vol. 3, no. 11, pp. 39–41. https://doi.org/10.1145/219717.219748CrossRef

Titel: Developing a Semantic Role Labeling System in Scientific and Technical Texts on Aviation and Astronautics
verfasst von: Ju. I. Butenko
M. L. Galetka
E. E. Sineva
Publikationsdatum: 01.10.2022
Verlag: Pleiades Publishing
Erschienen in: Automatic Documentation and Mathematical Linguistics / Ausgabe 5/2022
Print ISSN: 0005-1055
Elektronische ISSN: 1934-8371
DOI: https://doi.org/10.3103/S0005105522050077

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Weitere Artikel der Ausgabe 5/2022

Patterns of Using the Z-Score for Text Classification Purposes

Approaching the Standard of Knowledge Representation in the Web by Algebraic Means

On the Problem of AI-Tools Application in Digital Control Systems

The Phenomenon of Insight in the Light of Information Theory

Optimized Fuzzy Inference for Sugeno-Type Systems

Methodological Foundations and Prototype of a Semantic Artificial Intelligence System