[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/authors/greg-durrett/) [home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/) / Greg Durrett # Greg Durrett 5 paper cards in Mova's library · 2013–2023 Greg Durrett wrote 5 of the papers Mova reads, in 2013–2023, most of them on [Discourse analysis](https://md.mova.elamur.ai/free/en/docs/papers/topics/discourse-analysis/), [Coreference resolution](https://md.mova.elamur.ai/free/en/docs/papers/topics/coreference-resolution/), [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/); often with [Junyi Jessy Li](https://md.mova.elamur.ai/free/en/docs/papers/authors/junyi-jessy-li/), [Wei-Jen Ko](https://md.mova.elamur.ai/free/en/docs/papers/authors/wei-jen-ko/), [Yating Wu](https://md.mova.elamur.ai/free/en/docs/papers/authors/yating-wu/). [OpenAlex](https://openalex.org/A5015133105) · [ORCID](https://orcid.org/0000-0002-7061-7298) · [**h-index 35**](https://openalex.org/authors/A5015133105) · [4867 citations](https://openalex.org/authors/A5015133105) · [236 works](https://openalex.org/works?filter=author.id:A5015133105) — openalex, 2026-10-18 The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Greg+Durrett) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Greg+Durrett) · [dblp](https://dblp.org/search?q=Greg+Durrett) · [ACL Anthology](https://aclanthology.org/search/?q=Greg+Durrett) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Greg+Durrett) Topics: [Discourse analysis](https://md.mova.elamur.ai/free/en/docs/papers/topics/discourse-analysis/) 2 · [Coreference resolution](https://md.mova.elamur.ai/free/en/docs/papers/topics/coreference-resolution/) 1 · [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/) 1 ## Papers 1. **[Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion](https://md.mova.elamur.ai/free/en/docs/papers/ko-2023-discourse-analysis-questions-answers-parsing/)** *(2023)* The paper builds the first parser for QUD dependency structures: every sentence after the first gets an anchor sentence earlier in the document and a free-form question, raised by the anchor, that the sentence answers. 2. **[QUDeval: The Evaluation of Questions Under Discussion Discourse Parsing](https://md.mova.elamur.ai/free/en/docs/papers/wu-2023-qudeval-evaluation-questions-under-discussion/)** *(2023)* A QUD dependency parse links each answer sentence to an earlier anchor sentence by an implicit question; evaluating such free-form questions has been manual. 3. **[Inquisitive Question Generation for High Level Text Comprehension](https://md.mova.elamur.ai/free/en/docs/papers/ko-2020-inquisitive-question-generation-high-level/)** *(2020)* INQUISITIVE is a set of about 19K questions that crowd workers asked while reading the first five sentences of 1,500 news articles one sentence at a time, each question tied to the span it is about and checked not to be answered already by the earlier text. 4. **[Easy Victories and Uphill Battles in Coreference Resolution](https://md.mova.elamur.ai/free/en/docs/papers/durrett-2013-easy-victories-uphill-battles-coreference/)** *(2013)* The paper builds a learning-based coreference system, the Berkeley system, that uses only a small set of homogeneous feature templates over shallow mention properties (head match, exact string match, and syntactic and discourse features) instead of many hand-built heuristics. 5. **[Supervised Learning of Complete Morphological Paradigms](https://md.mova.elamur.ai/free/en/docs/papers/lex-2013-durrett-morphological-paradigms/)** *(2013)* The paper predicts every inflected form of a lemma from its base form. First it aligns base forms with inflected forms to build an inventory of orthographic transformation rules, prefix, suffix and stem changes. Then a semi-Markov conditional random field learns where each rule applies, and it predicts all forms of… ## Co-authors (10) - [Junyi Jessy Li](https://md.mova.elamur.ai/free/en/docs/papers/authors/junyi-jessy-li/) ×3 - [Wei-Jen Ko](https://md.mova.elamur.ai/free/en/docs/papers/authors/wei-jen-ko/) ×2 - [Yating Wu](https://md.mova.elamur.ai/free/en/docs/papers/authors/yating-wu/) ×2 - Cutter Dalton - [Dan Klein](https://md.mova.elamur.ai/free/en/docs/papers/authors/dan-klein/) - Dananjay Srinivas - John DeNero - Ritika Mangla - Te-yuan Chen - Yiyan Huang - [← previous Gosse Bouma](https://md.mova.elamur.ai/free/en/docs/papers/authors/gosse-bouma/) - [next → Gregory R. Darwin](https://md.mova.elamur.ai/free/en/docs/papers/authors/gregory-r-darwin/) - [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)