MEDVEĎ, Marek, Aleš HORÁK and Radoslav SABOL. Employing Sentence Context in Czech Answer Selection. In Sojka P., Kopeček I., Pala K., Horák A. Text, Speech, and Dialogue. TSD 2020. Switzerland: Springer, Cham, 2020, p. 112-121. ISBN 978-3-030-58322-4. Available from: https://dx.doi.org/10.1007/978-3-030-58323-1_12.
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Basic information
Original name Employing Sentence Context in Czech Answer Selection
Authors MEDVEĎ, Marek (703 Slovakia, guarantor, belonging to the institution), Aleš HORÁK (203 Czech Republic, belonging to the institution) and Radoslav SABOL (703 Slovakia, belonging to the institution).
Edition Switzerland, Text, Speech, and Dialogue. TSD 2020, p. 112-121, 10 pp. 2020.
Publisher Springer, Cham
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Switzerland
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
Impact factor Impact factor: 0.402 in 2005
RIV identification code RIV/00216224:14330/20:00114572
Organization unit Faculty of Informatics
ISBN 978-3-030-58322-4
ISSN 0302-9743
Doi http://dx.doi.org/10.1007/978-3-030-58323-1_12
UT WoS 000611543200012
Keywords in English question answering;answer selection;Czech;answer context;morphologically rich languages
Tags firank_B
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 29/4/2021 08:12.
Abstract
Question answering (QA) of non-mainstream languages requires specific adaptations of the current methods tested primarily with very large English resources. In this paper, we present the results of improving the QA answer selection task by extending the input candidate sentence with selected information from preceding sentence context. The described model represents the best published answer selection model for the Czech language as an example of a morphologically rich language. The text contains thorough evaluation of the new method including model hyperparameter combinations and detailed error discussion. The winning models have improved the previous best results by 4% reaching the mean average precision of 82.91%.
Links
GA18-23891S, research and development projectName: Hyperintensionální usuzování nad texty přirozeného jazyka
Investor: Czech Science Foundation
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