A Two-stage Sieve Approach for Quote Attribution

Grace Muzny, Michael Fang, Angel Chang, Dan Jurafsky


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.
Anthology ID:
E17-1044
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Mirella Lapata, Phil Blunsom, Alexander Koller
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
460–470
Language:
URL:
https://aclanthology.org/E17-1044
DOI:
Bibkey:
Cite (ACL):
Grace Muzny, Michael Fang, Angel Chang, and Dan Jurafsky. 2017. A Two-stage Sieve Approach for Quote Attribution. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 460–470, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
A Two-stage Sieve Approach for Quote Attribution (Muzny et al., EACL 2017)
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PDF:
https://aclanthology.org/E17-1044.pdf