@inproceedings{dayrell-etal-2012-rhetorical,
title = "Rhetorical Move Detection in {E}nglish Abstracts: Multi-label Sentence Classifiers and their Annotated Corpora",
author = "Dayrell, Carmen and
Candido Jr., Arnaldo and
Lima, Gabriel and
Machado Jr., Danilo and
Copestake, Ann and
Feltrim, Val{\'e}ria and
Tagnin, Stella and
Aluisio, Sandra",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Do{\u{g}}an, Mehmet U{\u{g}}ur and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/734_Paper.pdf",
pages = "1604--1609",
abstract = "The relevance of automatically identifying rhetorical moves in scientific texts has been widely acknowledged in the literature. This study focuses on abstracts of standard research papers written in English and aims to tackle a fundamental limitation of current machine-learning classifiers: they are mono-labeled, that is, a sentence can only be assigned one single label. However, such approach does not adequately reflect actual language use since a move can be realized by a clause, a sentence, or even several sentences. Here, we present MAZEA (Multi-label Argumentative Zoning for English Abstracts), a multi-label classifier which automatically identifies rhetorical moves in abstracts but allows for a given sentence to be assigned as many labels as appropriate. We have resorted to various other NLP tools and used two large training corpora: (i) one corpus consists of 645 abstracts from physical sciences and engineering (PE) and (ii) the other corpus is made up of 690 from life and health sciences (LH). This paper presents our preliminary results and also discusses the various challenges involved in multi-label tagging and works towards satisfactory solutions. In addition, we also make our two training corpora publicly available so that they may serve as benchmark for this new task.",
}
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<abstract>The relevance of automatically identifying rhetorical moves in scientific texts has been widely acknowledged in the literature. This study focuses on abstracts of standard research papers written in English and aims to tackle a fundamental limitation of current machine-learning classifiers: they are mono-labeled, that is, a sentence can only be assigned one single label. However, such approach does not adequately reflect actual language use since a move can be realized by a clause, a sentence, or even several sentences. Here, we present MAZEA (Multi-label Argumentative Zoning for English Abstracts), a multi-label classifier which automatically identifies rhetorical moves in abstracts but allows for a given sentence to be assigned as many labels as appropriate. We have resorted to various other NLP tools and used two large training corpora: (i) one corpus consists of 645 abstracts from physical sciences and engineering (PE) and (ii) the other corpus is made up of 690 from life and health sciences (LH). This paper presents our preliminary results and also discusses the various challenges involved in multi-label tagging and works towards satisfactory solutions. In addition, we also make our two training corpora publicly available so that they may serve as benchmark for this new task.</abstract>
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%0 Conference Proceedings
%T Rhetorical Move Detection in English Abstracts: Multi-label Sentence Classifiers and their Annotated Corpora
%A Dayrell, Carmen
%A Candido Jr., Arnaldo
%A Lima, Gabriel
%A Machado Jr., Danilo
%A Copestake, Ann
%A Feltrim, Valéria
%A Tagnin, Stella
%A Aluisio, Sandra
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Doğan, Mehmet Uğur
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12)
%D 2012
%8 May
%I European Language Resources Association (ELRA)
%C Istanbul, Turkey
%F dayrell-etal-2012-rhetorical
%X The relevance of automatically identifying rhetorical moves in scientific texts has been widely acknowledged in the literature. This study focuses on abstracts of standard research papers written in English and aims to tackle a fundamental limitation of current machine-learning classifiers: they are mono-labeled, that is, a sentence can only be assigned one single label. However, such approach does not adequately reflect actual language use since a move can be realized by a clause, a sentence, or even several sentences. Here, we present MAZEA (Multi-label Argumentative Zoning for English Abstracts), a multi-label classifier which automatically identifies rhetorical moves in abstracts but allows for a given sentence to be assigned as many labels as appropriate. We have resorted to various other NLP tools and used two large training corpora: (i) one corpus consists of 645 abstracts from physical sciences and engineering (PE) and (ii) the other corpus is made up of 690 from life and health sciences (LH). This paper presents our preliminary results and also discusses the various challenges involved in multi-label tagging and works towards satisfactory solutions. In addition, we also make our two training corpora publicly available so that they may serve as benchmark for this new task.
%U http://www.lrec-conf.org/proceedings/lrec2012/pdf/734_Paper.pdf
%P 1604-1609
Markdown (Informal)
[Rhetorical Move Detection in English Abstracts: Multi-label Sentence Classifiers and their Annotated Corpora](http://www.lrec-conf.org/proceedings/lrec2012/pdf/734_Paper.pdf) (Dayrell et al., LREC 2012)
ACL
- Carmen Dayrell, Arnaldo Candido Jr., Gabriel Lima, Danilo Machado Jr., Ann Copestake, Valéria Feltrim, Stella Tagnin, and Sandra Aluisio. 2012. Rhetorical Move Detection in English Abstracts: Multi-label Sentence Classifiers and their Annotated Corpora. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), pages 1604–1609, Istanbul, Turkey. European Language Resources Association (ELRA).