Standard

Improving keyphrase extraction using ll-ranking. / Popova, Svetlana; Danilova, Vera; Alexandrov, Mikhail; Cardiff, John.

Advances in Intelligent Systems and Computing IV- Selected Papers from the International Conference on Computer Science and Information Technologies, CSIT 2019. ред. / Natalya Shakhovska; Mykola O. Medykovskyy. Springer Nature, 2020. стр. 567-578 (Advances in Intelligent Systems and Computing; Том 1080 AISC).

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование

Harvard

Popova, S, Danilova, V, Alexandrov, M & Cardiff, J 2020, Improving keyphrase extraction using ll-ranking. в N Shakhovska & MO Medykovskyy (ред.), Advances in Intelligent Systems and Computing IV- Selected Papers from the International Conference on Computer Science and Information Technologies, CSIT 2019. Advances in Intelligent Systems and Computing, Том. 1080 AISC, Springer Nature, стр. 567-578, 14th International Scientific and Technical Conference on Computer Science and Information Technologies, CSIT 2019, Lviv, Украина, 17/09/19. https://doi.org/10.1007/978-3-030-33695-0_38

APA

Popova, S., Danilova, V., Alexandrov, M., & Cardiff, J. (2020). Improving keyphrase extraction using ll-ranking. в N. Shakhovska, & M. O. Medykovskyy (Ред.), Advances in Intelligent Systems and Computing IV- Selected Papers from the International Conference on Computer Science and Information Technologies, CSIT 2019 (стр. 567-578). (Advances in Intelligent Systems and Computing; Том 1080 AISC). Springer Nature. https://doi.org/10.1007/978-3-030-33695-0_38

Vancouver

Popova S, Danilova V, Alexandrov M, Cardiff J. Improving keyphrase extraction using ll-ranking. в Shakhovska N, Medykovskyy MO, Редакторы, Advances in Intelligent Systems and Computing IV- Selected Papers from the International Conference on Computer Science and Information Technologies, CSIT 2019. Springer Nature. 2020. стр. 567-578. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-33695-0_38

Author

Popova, Svetlana ; Danilova, Vera ; Alexandrov, Mikhail ; Cardiff, John. / Improving keyphrase extraction using ll-ranking. Advances in Intelligent Systems and Computing IV- Selected Papers from the International Conference on Computer Science and Information Technologies, CSIT 2019. Редактор / Natalya Shakhovska ; Mykola O. Medykovskyy. Springer Nature, 2020. стр. 567-578 (Advances in Intelligent Systems and Computing).

BibTeX

@inproceedings{ad83588b7a924badb45c7af625d4968f,
title = "Improving keyphrase extraction using ll-ranking",
abstract = "Keyphrases provide a concise representation of the main content of a document and can be effectively used within information retrieval systems. In the paper, we deal with the keyphrase extraction problem when a given number of keyphrases for a text should be extracted. The research is focused on the keyphrase candidates ranking stage. In the domain, the question remains open of whether the keyphrase extraction quality can be improved by putting limits on the number of phrases of different lengths extracted during candidate ranking. We assume that the quality of resulting keyphrases can be enhanced if we introduce Limitations on the number of phrases of specific Lengths in the resulting set (LL-ranking strategy). The experiments are performed on the well-known INSPEC dataset of scientific abstracts. The obtained results show that the proposed limitations help to significantly increase the quality of extracted keyphrases in terms of P recision and F 1.",
keywords = "Keyphrase candidates ranking, Keyphrase extraction, Length feature in keyphrase extraction problem",
author = "Svetlana Popova and Vera Danilova and Mikhail Alexandrov and John Cardiff",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 14th International Scientific and Technical Conference on Computer Science and Information Technologies, CSIT 2019 ; Conference date: 17-09-2019 Through 20-09-2019",
year = "2020",
doi = "10.1007/978-3-030-33695-0_38",
language = "English",
isbn = "9783030336943",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Nature",
pages = "567--578",
editor = "Natalya Shakhovska and Medykovskyy, {Mykola O.}",
booktitle = "Advances in Intelligent Systems and Computing IV- Selected Papers from the International Conference on Computer Science and Information Technologies, CSIT 2019",
address = "Germany",

}

RIS

TY - GEN

T1 - Improving keyphrase extraction using ll-ranking

AU - Popova, Svetlana

AU - Danilova, Vera

AU - Alexandrov, Mikhail

AU - Cardiff, John

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020

Y1 - 2020

N2 - Keyphrases provide a concise representation of the main content of a document and can be effectively used within information retrieval systems. In the paper, we deal with the keyphrase extraction problem when a given number of keyphrases for a text should be extracted. The research is focused on the keyphrase candidates ranking stage. In the domain, the question remains open of whether the keyphrase extraction quality can be improved by putting limits on the number of phrases of different lengths extracted during candidate ranking. We assume that the quality of resulting keyphrases can be enhanced if we introduce Limitations on the number of phrases of specific Lengths in the resulting set (LL-ranking strategy). The experiments are performed on the well-known INSPEC dataset of scientific abstracts. The obtained results show that the proposed limitations help to significantly increase the quality of extracted keyphrases in terms of P recision and F 1.

AB - Keyphrases provide a concise representation of the main content of a document and can be effectively used within information retrieval systems. In the paper, we deal with the keyphrase extraction problem when a given number of keyphrases for a text should be extracted. The research is focused on the keyphrase candidates ranking stage. In the domain, the question remains open of whether the keyphrase extraction quality can be improved by putting limits on the number of phrases of different lengths extracted during candidate ranking. We assume that the quality of resulting keyphrases can be enhanced if we introduce Limitations on the number of phrases of specific Lengths in the resulting set (LL-ranking strategy). The experiments are performed on the well-known INSPEC dataset of scientific abstracts. The obtained results show that the proposed limitations help to significantly increase the quality of extracted keyphrases in terms of P recision and F 1.

KW - Keyphrase candidates ranking

KW - Keyphrase extraction

KW - Length feature in keyphrase extraction problem

UR - http://www.scopus.com/inward/record.url?scp=85076971995&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-33695-0_38

DO - 10.1007/978-3-030-33695-0_38

M3 - Conference contribution

SN - 9783030336943

T3 - Advances in Intelligent Systems and Computing

SP - 567

EP - 578

BT - Advances in Intelligent Systems and Computing IV- Selected Papers from the International Conference on Computer Science and Information Technologies, CSIT 2019

A2 - Shakhovska, Natalya

A2 - Medykovskyy, Mykola O.

PB - Springer Nature

T2 - 14th International Scientific and Technical Conference on Computer Science and Information Technologies, CSIT 2019

Y2 - 17 September 2019 through 20 September 2019

ER -

ID: 88341382