Systems and Means of Informatics
2022, Volume 32, Issue 3, pp 92-102
METHODS FOR RETRIEVAL OF IMPLICIT LOGICAL-SEMANTIC RELATIONS FROM MONOLINGUAL TEXTS
Abstract
The paper focuses on the problem of targeted retrieval of fragments with implicit logical-semantic relations (LSRs) from monolingual natural language texts. In order to answer the question whether such retrieval is possible, a survey of works with the results of implicit LSRs studies has been conducted.
The aim of the survey is to identify the methods used in these works to create arrays of text fragments with implicit LSRs. It was revealed that mainly two methods are used: (i) automatic generation of fragments with implicit LSRs using fragments with explicit LSRs; and (ii) exporting of fragments with implicit LSRs from manually annotated text corpora. Neither of these techniques can be considered as a retrieval method because they do not imply detection of fragments with implicit LSRs in the natural language text and their subsequent extraction. Therefore, it can be stated that for monolingual texts, the task of targeted retrieval of such fragments currently remains unsolved.
[+] References (22)
- Roth, M., R. Tsarfaty, and Y. Goldberg, eds. 2021. 1st Workshop on Understanding Implicit and Underspecified Language Proceedings. Association for Computational Linguistics. 83 p.
- Pyatkin, V., D. Fried, and T. Anthonio, eds. 2022. 2nd Workshop on Understanding Implicit and Underspecified Language Proceedings. Seattle, WA: Association for Computational Linguistics. 41 p.
- Goncharov, A. A., and O.Yu. Inkova. 2020. Implitsitnye logiko-semanticheskie otnosheniya i metod ikh poiska v parallel'nykh tekstakh [Implicit logical-semantic relations and a method of their identification in parallel texts]. Computational Linguistics and Intellectual Technologies: Papers from the Annual Conference (International) "Dialogue" . Moscow: RSHI. 19(26):310-320.
- Inkova, O.Yu. 2019. Logiko-semanticheskie otnosheniya: Problemy klassifikatsii [Logical-semantic relations: Classification problems]. Svyaznost' teksta: mereologicheskie logiko-semanticheskie otnosheniya [Text coherence: Mereological logical semantic relations]. Moscow: LRC Publishing House. 11-98.
- Goncharov, A. A. 2021. Klassifikatsii vnutritekstovykh otnosheniy: Osnovaniya
i printsipy strukturirovaniya [Classifications of intratextual relations: Bases and structuring principles]. Voprosy yazykoznaniya [Topics in the Study of Language] 3:97-119.
- Yartseva, V.N., ed. 1998. Yazykoznanie: Bol'shoy entsiklopedicheskiy slovar' [Linguistics. Great encyclopedic dictionary]. 2nd ed. Moscow: Bol'shaya Rossiyskaya entsiklopediya. 685 p.
- Taboada, M. 2006. Discourse markers as signals (or not) of rhetorical relations. J. Pragmatics 38(4):567-592.
- Webber, B., R. Prasad, A. Lee, and A. Joshi. 2019. The Penn Discourse Treebank 3.0 annotation manual. Available at: https://catalog.ldc.upenn.edu/docs/ LDC2019T05/PDTB3-Annotation-Manual.pdf (accessed August 24, 2022).
- Marcu, D., and A. Echihabi. 2002. An unsupervised approach to recognizing discourse relations. 40th Annual Meeting of the Association for Computational Linguistics Proceedings. Philadelphia, PA: Association for Computational Linguistics. 368-375.
- Pitler, E., M. Raghupathy, H. Mehta, A. Nenkova, A. Lee, and A. Joshi. 2008. Easily identifiable discourse relations. 22nd Conference (International) on Computational Linguistics Proceedings. Manchester, U.K. 87-90.
- In'kova, O. Yu., ed. 2018. Semantika konnektorov: kontrastivnoe issledovanie [Semantics of connectives: Contrastive study]. Moscow: TORUS PRESS. 368 p.
- Pitler, E., A. Louis, and A. Nenkova. 2009. Automatic sense prediction for implicit discourse relations in text. Joint Conference of the 47th Annual Meeting of the ACL and the 4th Joint Conference (International) on Natural Language Processing of the AFNLP Proceedings. Singapore, Suntec: Association for Computational Linguistics. 683-691.
- Braud, Chl., and P. Denis. 2013. Identification automatique des relations discursives "implicites" a partir de donnees annotees et de corpus bruts. Actes de la 20e conference sur le Traitement Automatique des Langues Naturelles. Les Sables d'Olonne, France: ATALA. 1:104-117.
- Braud, Chl., and P. Denis. 2014. Identifier les relations discursives implicites en combinant donnees naturelles et donnees artificielles. Traitement Automatique Langues 55(1): 135-165.
- Braud, Chl. 2015. Identification automatique des relations discursives implicites a partir de corpus annotes et de donnees brutes. Paris: Universite Paris Diderot. 221 p.
- Blair-Goldensohn, S., K. R. McKeown, and O. C. Rambow. 2007. Building and refining rhetorical-semantic relationmodels. Human Language Technologies Conference Proceedings. Rochester, New York: Association for Computational Linguistics. 428435.
- Penn Discourse Treebank Project. Available at: https://www.seas.upenn.edu/~pdtb
(accessed August 24, 2022).
- Sporleder, C., and A. Lascarides. 2008. Using automatically labelled examples to classify rhetorical relations: An assessment. Nat. Lang. Eng. 14(3):369-416.
- Lin, Z, M.-Y. Kan, and H.T. Ng. 2009. Recognizing implicit discourse relations in the Penn Discourse Treebank. Conference on Empirical Methods in Natural Language Processing Proceedings. Singapore: Association for Computational Linguistics. 343351.
- Park, J., andC. Cardie. 2012. Improving implicit discourse relation recognition through feature set optimization. 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue Proceedings. Seoul, South Korea : Association for Computational Linguistics. 108-112.
- Rutherford, A. T., and N. Xue. 2014. Discovering implicit discourse relations through brown cluster pair representation and coreference patterns. 14th Conference of the European Chapter of the Association for Computational Linguistics Proceedings. Gothenburg, Sweden: Association for Computational Linguistics. 645-654.
- Liang, L., Zh. Zhao, and B. Webber. 2020. Extending implicit discourse relation recognition to the PDTB-3. 1st Workshop on Computational Approaches to Discourse Proceedings. Association for Computational Linguistics. 135-147.
[+] About this article
Title
METHODS FOR RETRIEVAL OF IMPLICIT LOGICAL-SEMANTIC RELATIONS FROM MONOLINGUAL TEXTS
Journal
Systems and Means of Informatics
Volume 32, Issue 3, pp 92-102
Cover Date
2022-06-11
DOI
10.14357/08696527220309
Print ISSN
0869-6527
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
implicitness; implicit logical-semantic relations; targeted retrieval; natural language processing; information extraction
Authors
A. A. Goncharov
Author Affiliations
Federal Research Center "Computer Science and Control", Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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