A Two-stage Sieve Approach for Quote Attribution
Grace Muzny, Michael Fang, Anne Lynn S. Chang, Dan Jurafsky · 2017
We present a deterministic sieve-based system for attributing quotations in literary text and a new dataset: QuoteLi3 1 .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 twostage 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 either for overall performance or for the high precision appropriate for many 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%.