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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, Coreference resolution, Dependency parsing; often with Junyi Jessy Li, Wei-Jen Ko, Yating Wu.

OpenAlex · ORCID · h-index 35 · 4867 citations · 236 works — 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 · Semantic Scholar · dblp · ACL Anthology · ORCID

Topics: Discourse analysis 2 · Coreference resolution 1 · Dependency parsing 1

Papers

  1. Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion (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 (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 (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 (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 (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)

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