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%.

Online resources, such as Wiktionary, provide an accurate but incomplete source of idiomatic phrases.In this paper, we study the problem of automatically identifying idiomatic dictionary entries with such resources.We train an idiom classifier on a newly gathered corpus of over 60,000 Wiktionary multi-word definitions, incorporating features that model whether phrase meanings are constructed compositionally.Experiments demonstrate that the learned classifier can provide high quality idiom labels, more than doubling the number of idiomatic entries from 7,764 to 18,155 at precision levels of over 65%.These gains also translate to idiom detection in sentences, by simply using known word sense disambiguation algorithms to match phrases to their definitions.In a set of Wiktionary definition example sentences, the more complete set of idioms boosts detection recall by over 28 percentage points.