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Using polyadic formal contexts for information extraction from natural language texts. / Bogatyrev, Mikhail; Mitrofanova, Olga.

In: CEUR Workshop Proceedings, Vol. 2813, 2021, p. 140-154.

Research output: Contribution to journal › Conference article › peer-review

Harvard

Bogatyrev, M & Mitrofanova, O 2021, 'Using polyadic formal contexts for information extraction from natural language texts', CEUR Workshop Proceedings, vol. 2813, pp. 140-154.

APA

Bogatyrev, M., & Mitrofanova, O. (2021). Using polyadic formal contexts for information extraction from natural language texts. CEUR Workshop Proceedings, 2813, 140-154.

Vancouver

Bogatyrev M, Mitrofanova O. Using polyadic formal contexts for information extraction from natural language texts. CEUR Workshop Proceedings. 2021;2813:140-154.

Author

Bogatyrev, Mikhail ; Mitrofanova, Olga. / Using polyadic formal contexts for information extraction from natural language texts. In: CEUR Workshop Proceedings. 2021 ; Vol. 2813. pp. 140-154.

BibTeX

@article{150ab49915974c7eb3a05e6f9e72f548,
title = "Using polyadic formal contexts for information extraction from natural language texts",
abstract = "The paper considers the use of elements of Formal Concept Analysis – multidimensional or polyadic formal contexts – to extract information from natural language texts. We propose the method for constructing polyadic formal contexts by means of Semantic Role Labeling and Abstract Meaning Representation (AMR) of texts. Using semantic role labeling, a conceptual graph is created for each sentence of the text, and a specific scheme of abstract meaning representation of the sentence is developed based on its elements. The polyadic formal context is a multidimensional tensor, whose points are elements of an AMR scheme. To extract information from a polyadic formal context, data associations as sub-contexts of the original context are built. Each such subcontext is associated with a specific element of the AMR scheme. Queries to associations return responses that preserve the meaning of the phrases according to the AMR scheme. The method was tested in the task of finding dependencies between texts on the corpus of abstracts of scientific articles on biomedical subjects of the PubMed system.",
keywords = "Abstract meaning representation, Information retrieval, Polyadic formal context",
author = "Mikhail Bogatyrev and Olga Mitrofanova",
note = "Publisher Copyright: Copyright {\textcopyright} 2020 for this paper by its authors.; Internet and Modern Society ; Conference date: 17-06-2020 Through 20-06-2020",
year = "2021",
language = "English",
volume = "2813",
pages = "140--154",
journal = "CEUR Workshop Proceedings",
issn = "1613-0073",
publisher = "RWTH Aahen University",
url = "http://ims.ifmo.ru/ru/pages/2/programma.htm",

}

RIS

TY - JOUR

T1 - Using polyadic formal contexts for information extraction from natural language texts

AU - Bogatyrev, Mikhail

AU - Mitrofanova, Olga

N1 - Conference code: 23

PY - 2021

Y1 - 2021

N2 - The paper considers the use of elements of Formal Concept Analysis – multidimensional or polyadic formal contexts – to extract information from natural language texts. We propose the method for constructing polyadic formal contexts by means of Semantic Role Labeling and Abstract Meaning Representation (AMR) of texts. Using semantic role labeling, a conceptual graph is created for each sentence of the text, and a specific scheme of abstract meaning representation of the sentence is developed based on its elements. The polyadic formal context is a multidimensional tensor, whose points are elements of an AMR scheme. To extract information from a polyadic formal context, data associations as sub-contexts of the original context are built. Each such subcontext is associated with a specific element of the AMR scheme. Queries to associations return responses that preserve the meaning of the phrases according to the AMR scheme. The method was tested in the task of finding dependencies between texts on the corpus of abstracts of scientific articles on biomedical subjects of the PubMed system.

AB - The paper considers the use of elements of Formal Concept Analysis – multidimensional or polyadic formal contexts – to extract information from natural language texts. We propose the method for constructing polyadic formal contexts by means of Semantic Role Labeling and Abstract Meaning Representation (AMR) of texts. Using semantic role labeling, a conceptual graph is created for each sentence of the text, and a specific scheme of abstract meaning representation of the sentence is developed based on its elements. The polyadic formal context is a multidimensional tensor, whose points are elements of an AMR scheme. To extract information from a polyadic formal context, data associations as sub-contexts of the original context are built. Each such subcontext is associated with a specific element of the AMR scheme. Queries to associations return responses that preserve the meaning of the phrases according to the AMR scheme. The method was tested in the task of finding dependencies between texts on the corpus of abstracts of scientific articles on biomedical subjects of the PubMed system.

KW - Abstract meaning representation

KW - Information retrieval

KW - Polyadic formal context

UR - http://www.scopus.com/inward/record.url?scp=85101607995&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85101607995

VL - 2813

SP - 140

EP - 154

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

T2 - Internet and Modern Society

Y2 - 17 June 2020 through 20 June 2020

ER -

ID: 85926981