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Document performance prediction for automatic text classification. / Penha, Gustavo; Campos, Raphael; Canuto, Sérgio; Gonçalves, Marcos André; Santos, Rodrygo L.T.

Advances in Information Retrieval : 41st European Conference on IR Research, ECIR 2019. ed. / Leif Azzopardi; Benno Stein; Norbert Fuhr; Claudia Hauff; Philipp Mayr; Djoerd Hiemstra. Cham : Springer Verlag, 2019. p. 132-139 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11438 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Penha, G, Campos, R, Canuto, S, Gonçalves, MA & Santos, RLT 2019, Document performance prediction for automatic text classification. in L Azzopardi, B Stein, N Fuhr, C Hauff, P Mayr & D Hiemstra (eds), Advances in Information Retrieval : 41st European Conference on IR Research, ECIR 2019. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11438 LNCS, Springer Verlag, Cham, pp. 132-139, 41st European Conference on Information Retrieval, ECIR 2019, Cologne, Germany, 14/04/19. https://doi.org/10.1007/978-3-030-15719-7_17

APA

Penha, G., Campos, R., Canuto, S., Gonçalves, M. A., & Santos, R. L. T. (2019). Document performance prediction for automatic text classification. In L. Azzopardi, B. Stein, N. Fuhr, C. Hauff, P. Mayr, & D. Hiemstra (Eds.), Advances in Information Retrieval : 41st European Conference on IR Research, ECIR 2019 (pp. 132-139). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11438 LNCS). Cham: Springer Verlag. https://doi.org/10.1007/978-3-030-15719-7_17

Vancouver

Penha G, Campos R, Canuto S, Gonçalves MA, Santos RLT. Document performance prediction for automatic text classification. In Azzopardi L, Stein B, Fuhr N, Hauff C, Mayr P, Hiemstra D, editors, Advances in Information Retrieval : 41st European Conference on IR Research, ECIR 2019. Cham: Springer Verlag. 2019. p. 132-139. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-15719-7_17

Author

Penha, Gustavo ; Campos, Raphael ; Canuto, Sérgio ; Gonçalves, Marcos André ; Santos, Rodrygo L.T. / Document performance prediction for automatic text classification. Advances in Information Retrieval : 41st European Conference on IR Research, ECIR 2019. editor / Leif Azzopardi ; Benno Stein ; Norbert Fuhr ; Claudia Hauff ; Philipp Mayr ; Djoerd Hiemstra. Cham : Springer Verlag, 2019. pp. 132-139 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{5592ec0dcb11488a91d47f60e98d9a3c,
title = "Document performance prediction for automatic text classification",
abstract = "Query performance prediction (QPP) is a fundamental task in information retrieval, which concerns predicting the effectiveness of a ranking model for a given query in the absence of relevance information. Despite being an active research area, this task has not yet been explored in the context of automatic text classification. In this paper, we study the task of predicting the effectiveness of a classifier for a given document, which we refer to as document performance prediction (DPP). Our experiments on several text classification datasets for both categorization and sentiment analysis attest the effectiveness and complementarity of several DPP inspired by related QPP approaches. Finally, we also explore the usefulness of DPP for improving the classification itself, by using them as additional features in a classification ensemble.",
keywords = "Automatic text classification, Performance prediction",
author = "Gustavo Penha and Raphael Campos and S{\'e}rgio Canuto and Gon{\cc}alves, {Marcos Andr{\'e}} and Santos, {Rodrygo L.T.}",
year = "2019",
doi = "10.1007/978-3-030-15719-7_17",
language = "English",
isbn = "978-3-030-15718-0",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "132--139",
editor = "Leif Azzopardi and Benno Stein and Norbert Fuhr and Claudia Hauff and Philipp Mayr and Djoerd Hiemstra",
booktitle = "Advances in Information Retrieval",

}

RIS

TY - GEN

T1 - Document performance prediction for automatic text classification

AU - Penha, Gustavo

AU - Campos, Raphael

AU - Canuto, Sérgio

AU - Gonçalves, Marcos André

AU - Santos, Rodrygo L.T.

PY - 2019

Y1 - 2019

N2 - Query performance prediction (QPP) is a fundamental task in information retrieval, which concerns predicting the effectiveness of a ranking model for a given query in the absence of relevance information. Despite being an active research area, this task has not yet been explored in the context of automatic text classification. In this paper, we study the task of predicting the effectiveness of a classifier for a given document, which we refer to as document performance prediction (DPP). Our experiments on several text classification datasets for both categorization and sentiment analysis attest the effectiveness and complementarity of several DPP inspired by related QPP approaches. Finally, we also explore the usefulness of DPP for improving the classification itself, by using them as additional features in a classification ensemble.

AB - Query performance prediction (QPP) is a fundamental task in information retrieval, which concerns predicting the effectiveness of a ranking model for a given query in the absence of relevance information. Despite being an active research area, this task has not yet been explored in the context of automatic text classification. In this paper, we study the task of predicting the effectiveness of a classifier for a given document, which we refer to as document performance prediction (DPP). Our experiments on several text classification datasets for both categorization and sentiment analysis attest the effectiveness and complementarity of several DPP inspired by related QPP approaches. Finally, we also explore the usefulness of DPP for improving the classification itself, by using them as additional features in a classification ensemble.

KW - Automatic text classification

KW - Performance prediction

UR - http://www.scopus.com/inward/record.url?scp=85064858139&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-15719-7_17

DO - 10.1007/978-3-030-15719-7_17

M3 - Conference contribution

SN - 978-3-030-15718-0

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

SP - 132

EP - 139

BT - Advances in Information Retrieval

A2 - Azzopardi, Leif

A2 - Stein, Benno

A2 - Fuhr, Norbert

A2 - Hauff, Claudia

A2 - Mayr, Philipp

A2 - Hiemstra, Djoerd

PB - Springer Verlag

CY - Cham

ER -

ID: 53627546