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Comparative Evaluation and Integration of Collocation Extraction Metrics. / Zakharov, Victor .

Text, Speech, and Dialogue: 20th International Conference, TSD 2017, Prague, Czech Republic, August 27-31, 2017, Proceedings. Cham : Springer Nature, 2017. p. 255-262 (Lecture Notes in Computer Science; Vol. 10415).

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

Harvard

Zakharov, V 2017, Comparative Evaluation and Integration of Collocation Extraction Metrics. in Text, Speech, and Dialogue: 20th International Conference, TSD 2017, Prague, Czech Republic, August 27-31, 2017, Proceedings. Lecture Notes in Computer Science, vol. 10415, Springer Nature, Cham, pp. 255-262, Text, Speech, and Dialogue, Prague, Czech Republic, 27/08/17.

APA

Zakharov, V. (2017). Comparative Evaluation and Integration of Collocation Extraction Metrics. In Text, Speech, and Dialogue: 20th International Conference, TSD 2017, Prague, Czech Republic, August 27-31, 2017, Proceedings (pp. 255-262). (Lecture Notes in Computer Science; Vol. 10415). Springer Nature.

Vancouver

Zakharov V. Comparative Evaluation and Integration of Collocation Extraction Metrics. In Text, Speech, and Dialogue: 20th International Conference, TSD 2017, Prague, Czech Republic, August 27-31, 2017, Proceedings. Cham: Springer Nature. 2017. p. 255-262. (Lecture Notes in Computer Science).

Author

Zakharov, Victor . / Comparative Evaluation and Integration of Collocation Extraction Metrics. Text, Speech, and Dialogue: 20th International Conference, TSD 2017, Prague, Czech Republic, August 27-31, 2017, Proceedings. Cham : Springer Nature, 2017. pp. 255-262 (Lecture Notes in Computer Science).

BibTeX

@inproceedings{d3fce3c615fb45db8ee2454e47441546,
title = "Comparative Evaluation and Integration of Collocation Extraction Metrics",
abstract = "The paper deals with collocation extraction from corpus data. A whole number of formulae have been created to integrate different factors that determine the association between the collocation components. The experiments are described which objective was to study the method of collocation extraction based on the statistical association measures. The work is focused on bigram collocations. The obtained data on the measure precision allow to establish to some degree that some measures are more precise than others. No measure is ideal, which is why various options of their integration are desirable and useful. We propose a number of parameters that allow to rank collocates in an combined list, namely, an average rank, a normalized rank and an optimized rank.",
keywords = "Collocation extraction , Association measures , Evaluation ‧ Ranking , Average rank ‧ , Normalized rank ‧, Optimized rank, Collocation extraction, Association measures, Evaluation, Ranking, Average rank, Normalized rank, Optimized rank",
author = "Victor Zakharov",
note = "Zakharov V. (2017) Comparative Evaluation and Integration of Collocation Extraction Metrics. In: Ek{\v s}tein K., Matou{\v s}ek V. (eds) Text, Speech, and Dialogue. TSD 2017. Lecture Notes in Computer Science, vol 10415. Springer, Cham. https://doi.org/10.1007/978-3-319-64206-2_29; Text, Speech, and Dialogue : 20th International Conference, TSD 2017 ; Conference date: 27-08-2017 Through 31-08-2017",
year = "2017",
language = "English",
isbn = "978-3-319-64205-5",
series = "Lecture Notes in Computer Science",
publisher = "Springer Nature",
pages = "255--262",
booktitle = "Text, Speech, and Dialogue",
address = "Germany",

}

RIS

TY - GEN

T1 - Comparative Evaluation and Integration of Collocation Extraction Metrics

AU - Zakharov, Victor

N1 - Zakharov V. (2017) Comparative Evaluation and Integration of Collocation Extraction Metrics. In: Ekštein K., Matoušek V. (eds) Text, Speech, and Dialogue. TSD 2017. Lecture Notes in Computer Science, vol 10415. Springer, Cham. https://doi.org/10.1007/978-3-319-64206-2_29

PY - 2017

Y1 - 2017

N2 - The paper deals with collocation extraction from corpus data. A whole number of formulae have been created to integrate different factors that determine the association between the collocation components. The experiments are described which objective was to study the method of collocation extraction based on the statistical association measures. The work is focused on bigram collocations. The obtained data on the measure precision allow to establish to some degree that some measures are more precise than others. No measure is ideal, which is why various options of their integration are desirable and useful. We propose a number of parameters that allow to rank collocates in an combined list, namely, an average rank, a normalized rank and an optimized rank.

AB - The paper deals with collocation extraction from corpus data. A whole number of formulae have been created to integrate different factors that determine the association between the collocation components. The experiments are described which objective was to study the method of collocation extraction based on the statistical association measures. The work is focused on bigram collocations. The obtained data on the measure precision allow to establish to some degree that some measures are more precise than others. No measure is ideal, which is why various options of their integration are desirable and useful. We propose a number of parameters that allow to rank collocates in an combined list, namely, an average rank, a normalized rank and an optimized rank.

KW - Collocation extraction

KW - Association measures

KW - Evaluation ‧ Ranking

KW - Average rank ‧

KW - Normalized rank ‧

KW - Optimized rank

KW - Collocation extraction

KW - Association measures

KW - Evaluation

KW - Ranking

KW - Average rank

KW - Normalized rank

KW - Optimized rank

UR - https://link.springer.com/chapter/10.1007/978-3-319-64206-2_29

M3 - Conference contribution

SN - 978-3-319-64205-5

T3 - Lecture Notes in Computer Science

SP - 255

EP - 262

BT - Text, Speech, and Dialogue

PB - Springer Nature

CY - Cham

T2 - Text, Speech, and Dialogue

Y2 - 27 August 2017 through 31 August 2017

ER -

ID: 71300483