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Contextual Predictability of Texts for Texts Processing and Understanding. / Krutchenko, Olga; Pronoza, Ekaterina; Yagunova, Elena; Timokhov, Viktor; Ivanets, Alexander.

Mining Intelligence and Knowledge Exploration - 7th International Conference, MIKE 2019, Proceedings. ed. / P. B.R.; Veena Thenkanidiyoor; Rajendra Prasath; Odelu Vanga. Springer Nature, 2020. p. 104-119 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11987 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Krutchenko, O, Pronoza, E, Yagunova, E, Timokhov, V & Ivanets, A 2020, Contextual Predictability of Texts for Texts Processing and Understanding. in P B.R., V Thenkanidiyoor, R Prasath & O Vanga (eds), Mining Intelligence and Knowledge Exploration - 7th International Conference, MIKE 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11987 LNAI, Springer Nature, pp. 104-119, 7th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2019, Veling, India, 19/12/19. https://doi.org/10.1007/978-3-030-66187-8_11

APA

Krutchenko, O., Pronoza, E., Yagunova, E., Timokhov, V., & Ivanets, A. (2020). Contextual Predictability of Texts for Texts Processing and Understanding. In P. B.R., V. Thenkanidiyoor, R. Prasath, & O. Vanga (Eds.), Mining Intelligence and Knowledge Exploration - 7th International Conference, MIKE 2019, Proceedings (pp. 104-119). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11987 LNAI). Springer Nature. https://doi.org/10.1007/978-3-030-66187-8_11

Vancouver

Krutchenko O, Pronoza E, Yagunova E, Timokhov V, Ivanets A. Contextual Predictability of Texts for Texts Processing and Understanding. In B.R. P, Thenkanidiyoor V, Prasath R, Vanga O, editors, Mining Intelligence and Knowledge Exploration - 7th International Conference, MIKE 2019, Proceedings. Springer Nature. 2020. p. 104-119. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-66187-8_11

Author

Krutchenko, Olga ; Pronoza, Ekaterina ; Yagunova, Elena ; Timokhov, Viktor ; Ivanets, Alexander. / Contextual Predictability of Texts for Texts Processing and Understanding. Mining Intelligence and Knowledge Exploration - 7th International Conference, MIKE 2019, Proceedings. editor / P. B.R. ; Veena Thenkanidiyoor ; Rajendra Prasath ; Odelu Vanga. Springer Nature, 2020. pp. 104-119 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{93bababfe152477d9c7dcb73049ab6ad,
title = "Contextual Predictability of Texts for Texts Processing and Understanding",
abstract = "This paper is the first part of contextual predictability model investigation for Russian, it is focused on linguistic and psychology interpretation of models, features, metrics and sets of features. The aim of this paper is to identify the dependence of the implementation of contextual predictability procedures on the genre characteristics of the text (or text collection): scientific vs. fictional. We construct a model predicting text elements and designate its features for texts of different genres and domains. We analyze various methods for studying contextual predictability, carry out a computational experiment against scientific and fictional texts, and verify its results by the experiment with informants (cloze-tests) and word embeddings (word2vec CBOW model). As a result, text processing model is built. It is evaluated based on the selected contextual predictability features and experiments with informants.",
keywords = "Cloze test, Conditional probability, Contextual predictability, Dice, Fiction texts, Informational entropy, Language model, Scientific corpora, Surprisal",
author = "Olga Krutchenko and Ekaterina Pronoza and Elena Yagunova and Viktor Timokhov and Alexander Ivanets",
note = "Funding Information: The authors acknowledge the RSF for the research grant 18-18-00114. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 7th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2019 ; Conference date: 19-12-2019 Through 22-12-2019",
year = "2020",
doi = "10.1007/978-3-030-66187-8_11",
language = "English",
isbn = "9783030661861",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "104--119",
editor = "P. B.R. and Veena Thenkanidiyoor and Rajendra Prasath and Odelu Vanga",
booktitle = "Mining Intelligence and Knowledge Exploration - 7th International Conference, MIKE 2019, Proceedings",
address = "Germany",

}

RIS

TY - GEN

T1 - Contextual Predictability of Texts for Texts Processing and Understanding

AU - Krutchenko, Olga

AU - Pronoza, Ekaterina

AU - Yagunova, Elena

AU - Timokhov, Viktor

AU - Ivanets, Alexander

N1 - Funding Information: The authors acknowledge the RSF for the research grant 18-18-00114. Publisher Copyright: © 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020

Y1 - 2020

N2 - This paper is the first part of contextual predictability model investigation for Russian, it is focused on linguistic and psychology interpretation of models, features, metrics and sets of features. The aim of this paper is to identify the dependence of the implementation of contextual predictability procedures on the genre characteristics of the text (or text collection): scientific vs. fictional. We construct a model predicting text elements and designate its features for texts of different genres and domains. We analyze various methods for studying contextual predictability, carry out a computational experiment against scientific and fictional texts, and verify its results by the experiment with informants (cloze-tests) and word embeddings (word2vec CBOW model). As a result, text processing model is built. It is evaluated based on the selected contextual predictability features and experiments with informants.

AB - This paper is the first part of contextual predictability model investigation for Russian, it is focused on linguistic and psychology interpretation of models, features, metrics and sets of features. The aim of this paper is to identify the dependence of the implementation of contextual predictability procedures on the genre characteristics of the text (or text collection): scientific vs. fictional. We construct a model predicting text elements and designate its features for texts of different genres and domains. We analyze various methods for studying contextual predictability, carry out a computational experiment against scientific and fictional texts, and verify its results by the experiment with informants (cloze-tests) and word embeddings (word2vec CBOW model). As a result, text processing model is built. It is evaluated based on the selected contextual predictability features and experiments with informants.

KW - Cloze test

KW - Conditional probability

KW - Contextual predictability

KW - Dice

KW - Fiction texts

KW - Informational entropy

KW - Language model

KW - Scientific corpora

KW - Surprisal

UR - http://www.scopus.com/inward/record.url?scp=85098289150&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-66187-8_11

DO - 10.1007/978-3-030-66187-8_11

M3 - Conference contribution

AN - SCOPUS:85098289150

SN - 9783030661861

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 104

EP - 119

BT - Mining Intelligence and Knowledge Exploration - 7th International Conference, MIKE 2019, Proceedings

A2 - B.R., P.

A2 - Thenkanidiyoor, Veena

A2 - Prasath, Rajendra

A2 - Vanga, Odelu

PB - Springer Nature

T2 - 7th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2019

Y2 - 19 December 2019 through 22 December 2019

ER -

ID: 73342010