G. Muzny, M. Fang, A. Chang, and D. Jurafsky. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, page 460--470. Valencia, Spain, Association for Computational Linguistics, (April 2017)
Abstract
We present a deterministic sieve-based system for attributing quotations in literary text and a new dataset: QuoteLi3. Quote attribution, determining who said what in a given text, is important for tasks like creating dialogue systems, and in newer areas like computational literary studies, where it creates opportunities to analyze novels at scale rather than only a few at a time. We release QuoteLi3, which contains more than 6,000 annotations linking quotes to speaker mentions and quotes to speaker entities, and introduce a new algorithm for quote attribution. Our two-stage algorithm first links quotes to mentions, then mentions to entities. Using two stages encapsulates difficult sub-problems and improves system performance. The modular design allows us to tune for overall performance or higher precision, which is useful for many real-world use cases. Our system achieves an average F-score of 87.5 across three novels, outperforming previous systems, and can be tuned for precision of 90.4 at a recall of 65.1.
Description
A Two-stage Sieve Approach for Quote Attribution - ACL Anthology
%0 Conference Paper
%1 muzny2017twostage
%A Muzny, Grace
%A Fang, Michael
%A Chang, Angel
%A Jurafsky, Dan
%B Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%C Valencia, Spain
%D 2017
%I Association for Computational Linguistics
%K acl citation extraction language natural nlp processing quotation quote
%P 460--470
%T A Two-stage Sieve Approach for Quote Attribution
%U https://www.aclweb.org/anthology/E17-1044
%X We present a deterministic sieve-based system for attributing quotations in literary text and a new dataset: QuoteLi3. Quote attribution, determining who said what in a given text, is important for tasks like creating dialogue systems, and in newer areas like computational literary studies, where it creates opportunities to analyze novels at scale rather than only a few at a time. We release QuoteLi3, which contains more than 6,000 annotations linking quotes to speaker mentions and quotes to speaker entities, and introduce a new algorithm for quote attribution. Our two-stage algorithm first links quotes to mentions, then mentions to entities. Using two stages encapsulates difficult sub-problems and improves system performance. The modular design allows us to tune for overall performance or higher precision, which is useful for many real-world use cases. Our system achieves an average F-score of 87.5 across three novels, outperforming previous systems, and can be tuned for precision of 90.4 at a recall of 65.1.
@inproceedings{muzny2017twostage,
abstract = {We present a deterministic sieve-based system for attributing quotations in literary text and a new dataset: QuoteLi3. Quote attribution, determining who said what in a given text, is important for tasks like creating dialogue systems, and in newer areas like computational literary studies, where it creates opportunities to analyze novels at scale rather than only a few at a time. We release QuoteLi3, which contains more than 6,000 annotations linking quotes to speaker mentions and quotes to speaker entities, and introduce a new algorithm for quote attribution. Our two-stage algorithm first links quotes to mentions, then mentions to entities. Using two stages encapsulates difficult sub-problems and improves system performance. The modular design allows us to tune for overall performance or higher precision, which is useful for many real-world use cases. Our system achieves an average F-score of 87.5 across three novels, outperforming previous systems, and can be tuned for precision of 90.4 at a recall of 65.1.},
added-at = {2020-10-20T11:50:26.000+0200},
address = {Valencia, Spain},
author = {Muzny, Grace and Fang, Michael and Chang, Angel and Jurafsky, Dan},
biburl = {https://www.bibsonomy.org/bibtex/205eb128a7b91cc0d95cc1d4ccc8a3f00/jaeschke},
booktitle = {Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
description = {A Two-stage Sieve Approach for Quote Attribution - ACL Anthology},
interhash = {2580e5376aa876365073103389245658},
intrahash = {05eb128a7b91cc0d95cc1d4ccc8a3f00},
keywords = {acl citation extraction language natural nlp processing quotation quote},
month = apr,
pages = {460--470},
publisher = {Association for Computational Linguistics},
timestamp = {2020-10-20T11:50:26.000+0200},
title = {A Two-stage Sieve Approach for Quote Attribution},
url = {https://www.aclweb.org/anthology/E17-1044},
year = 2017
}