Automatically detecting and attributing indirect quotations

Silvia Pareti; Tim O'Keefe; Ioannis Konstas; James R. Curran; Irena Koprinska

Automatically detecting and attributing indirect quotations

Silvia Pareti, Tim O'Keefe, Ioannis Konstas, James R. Curran, Irena Koprinska

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

Abstract

Direct quotations are used for opinion mining and information extraction as they have an easy to extract span and they can be attributed to a speaker with high accuracy. However, simply focusing on direct quotations ignores around half of all reported speech, which is in the form of indirect or mixed speech. This work presents the first large-scale experiments in indirect and mixed quotation extraction and attribution. We propose two methods of extracting all quote types from news articles and evaluate them on two large annotated corpora, one of which is a contribution of this work. We further show that direct quotation attribution methods can be successfully applied to indirect and mixed quotation attribution.

Original language	English
Title of host publication	Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing
Publisher	Association for Computational Linguistics
Pages	989-999
Number of pages	11
ISBN (Electronic)	9781937284978
Publication status	Published - Oct 2013
Event	2013 Conference on Empirical Methods in Natural Language Processing - Seattle, United States Duration: 18 Oct 2013 → 21 Oct 2013

Conference

Conference	2013 Conference on Empirical Methods in Natural Language Processing
Abbreviated title	EMNLP 2013
Country/Territory	United States
City	Seattle
Period	18/10/13 → 21/10/13

ASJC Scopus subject areas

Computational Theory and Mathematics
Information Systems
Computer Vision and Pattern Recognition

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https://aclanthology.org/D13-1101.pdfLicence: CC BY-NC-SA

Cite this

@inproceedings{b069848d61ab492987cfd6e808c2153f,

title = "Automatically detecting and attributing indirect quotations",

abstract = "Direct quotations are used for opinion mining and information extraction as they have an easy to extract span and they can be attributed to a speaker with high accuracy. However, simply focusing on direct quotations ignores around half of all reported speech, which is in the form of indirect or mixed speech. This work presents the first large-scale experiments in indirect and mixed quotation extraction and attribution. We propose two methods of extracting all quote types from news articles and evaluate them on two large annotated corpora, one of which is a contribution of this work. We further show that direct quotation attribution methods can be successfully applied to indirect and mixed quotation attribution.",

author = "Silvia Pareti and Tim O'Keefe and Ioannis Konstas and Curran, \{James R.\} and Irena Koprinska",

note = "Publisher Copyright: {\textcopyright} 2013 Association for Computational Linguistics.; 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013 ; Conference date: 18-10-2013 Through 21-10-2013",

year = "2013",

month = oct,

language = "English",

pages = "989--999",

booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",

publisher = "Association for Computational Linguistics",

}

Pareti, S, O'Keefe, T, Konstas, I, Curran, JR & Koprinska, I 2013, Automatically detecting and attributing indirect quotations. in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp. 989-999, 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, Washington, United States, 18/10/13. <https://aclanthology.org/D13-1101.pdf>

TY - GEN

T1 - Automatically detecting and attributing indirect quotations

AU - Pareti, Silvia

AU - O'Keefe, Tim

AU - Konstas, Ioannis

AU - Curran, James R.

AU - Koprinska, Irena

PY - 2013/10

Y1 - 2013/10

N2 - Direct quotations are used for opinion mining and information extraction as they have an easy to extract span and they can be attributed to a speaker with high accuracy. However, simply focusing on direct quotations ignores around half of all reported speech, which is in the form of indirect or mixed speech. This work presents the first large-scale experiments in indirect and mixed quotation extraction and attribution. We propose two methods of extracting all quote types from news articles and evaluate them on two large annotated corpora, one of which is a contribution of this work. We further show that direct quotation attribution methods can be successfully applied to indirect and mixed quotation attribution.

AB - Direct quotations are used for opinion mining and information extraction as they have an easy to extract span and they can be attributed to a speaker with high accuracy. However, simply focusing on direct quotations ignores around half of all reported speech, which is in the form of indirect or mixed speech. This work presents the first large-scale experiments in indirect and mixed quotation extraction and attribution. We propose two methods of extracting all quote types from news articles and evaluate them on two large annotated corpora, one of which is a contribution of this work. We further show that direct quotation attribution methods can be successfully applied to indirect and mixed quotation attribution.

UR - https://www.scopus.com/pages/publications/84926306375

M3 - Conference contribution

AN - SCOPUS:84926306375

SP - 989

EP - 999

BT - Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

PB - Association for Computational Linguistics

T2 - 2013 Conference on Empirical Methods in Natural Language Processing

Y2 - 18 October 2013 through 21 October 2013

ER -

Automatically detecting and attributing indirect quotations

Abstract

Conference

ASJC Scopus subject areas

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