A sequence labelling approach to quote attribution

Tim O'Keefe; Silvia Pareti; James R. Curran; Irena Koprinska; Matthew Honnibal

A sequence labelling approach to quote attribution

Tim O'Keefe, Silvia Pareti, James R. Curran, Irena Koprinska, Matthew Honnibal

School of Computing

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

66 Citations (Scopus)

Abstract

Quote extraction and attribution is the task of automatically extracting quotes from text and attributing each quote to its correct speaker. The present state-of-the-art system uses gold standard information from previous decisions in its features, which, when removed, results in a large drop in performance. We treat the problem as a sequence labelling task, which allows us to incorporate sequence features without using gold standard information. We present results on two new corpora and an augmented version of a third, achieving a new state-of-the-art for systems using only realistic features.

Original language	English
Title of host publication	EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference
Pages	790-799
Number of pages	10
Publication status	Published - 2012
Event	2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012 - Jeju Island, Korea, Republic of Duration: 12 Jul 2012 → 14 Jul 2012

Other

Other	2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012
Country/Territory	Korea, Republic of
City	Jeju Island
Period	12/07/12 → 14/07/12

Cite this

@inproceedings{61d147a32bbb4dc8be551debcc2d2fb5,

title = "A sequence labelling approach to quote attribution",

abstract = "Quote extraction and attribution is the task of automatically extracting quotes from text and attributing each quote to its correct speaker. The present state-of-the-art system uses gold standard information from previous decisions in its features, which, when removed, results in a large drop in performance. We treat the problem as a sequence labelling task, which allows us to incorporate sequence features without using gold standard information. We present results on two new corpora and an augmented version of a third, achieving a new state-of-the-art for systems using only realistic features.",

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

year = "2012",

language = "English",

isbn = "9781937284435",

pages = "790--799",

booktitle = "EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference",

note = "2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012 ; Conference date: 12-07-2012 Through 14-07-2012",

}

O'Keefe, T, Pareti, S, Curran, JR, Koprinska, I & Honnibal, M 2012, A sequence labelling approach to quote attribution. in EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference. pp. 790-799, 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012, Jeju Island, Korea, Republic of, 12/07/12.

A sequence labelling approach to quote attribution. / O'Keefe, Tim; Pareti, Silvia; Curran, James R. et al.
EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference. 2012. p. 790-799.

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

TY - GEN

T1 - A sequence labelling approach to quote attribution

AU - O'Keefe, Tim

AU - Pareti, Silvia

AU - Curran, James R.

AU - Koprinska, Irena

AU - Honnibal, Matthew

PY - 2012

Y1 - 2012

N2 - Quote extraction and attribution is the task of automatically extracting quotes from text and attributing each quote to its correct speaker. The present state-of-the-art system uses gold standard information from previous decisions in its features, which, when removed, results in a large drop in performance. We treat the problem as a sequence labelling task, which allows us to incorporate sequence features without using gold standard information. We present results on two new corpora and an augmented version of a third, achieving a new state-of-the-art for systems using only realistic features.

AB - Quote extraction and attribution is the task of automatically extracting quotes from text and attributing each quote to its correct speaker. The present state-of-the-art system uses gold standard information from previous decisions in its features, which, when removed, results in a large drop in performance. We treat the problem as a sequence labelling task, which allows us to incorporate sequence features without using gold standard information. We present results on two new corpora and an augmented version of a third, achieving a new state-of-the-art for systems using only realistic features.

UR - https://www.scopus.com/pages/publications/84883358626

M3 - Conference proceeding contribution

AN - SCOPUS:84883358626

SN - 9781937284435

SP - 790

EP - 799

BT - EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference

T2 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012

Y2 - 12 July 2012 through 14 July 2012

ER -

A sequence labelling approach to quote attribution

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