--- title: "Dan Jurafsky — papers and cards" description: "Dan Jurafsky: 4 paper cards in the Research library (2010–2013), with what each paper is about, the ideas Mova took from it, and the co-authors." canonical: "https://research.elamur.ai/free/en/authors/dan-jurafsky/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/dan-jurafsky/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Dan Jurafsky # Dan Jurafsky 4 paper cards in the Research library · 2010–2013 Dan Jurafsky wrote 4 of the papers Mova reads, in 2010–2013, most of them on [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/), [Dependency parsing](https://md.research.elamur.ai/free/en/topics/dependency-parsing/); often with [Heeyoung Lee](https://md.research.elamur.ai/free/en/authors/heeyoung-lee/), [Mihai Surdeanu](https://md.research.elamur.ai/free/en/authors/mihai-surdeanu/), [Nathanael Chambers](https://md.research.elamur.ai/free/en/authors/nathanael-chambers/). [OpenAlex](https://openalex.org/A5087088138) · [ORCID](https://orcid.org/0000-0002-6459-7745) · [**h-index 85**](https://openalex.org/authors/A5087088138) · [31947 citations](https://openalex.org/authors/A5087088138) · [471 works](https://openalex.org/works?filter=author.id:A5087088138) — openalex-snapshot, 2026-10-14 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=Dan+Jurafsky) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Dan+Jurafsky) · [dblp](https://dblp.org/search?q=Dan+Jurafsky) · [ACL Anthology](https://aclanthology.org/search/?q=Dan+Jurafsky) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Dan+Jurafsky) Topics: [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) 3 · [Dependency parsing](https://md.research.elamur.ai/free/en/topics/dependency-parsing/) 1 ## Papers 1. **[Deterministic Coreference Resolution Based on Entity-Centric, Precision-Ranked Rules](https://md.research.elamur.ai/free/en/papers/lee-2013-deterministic-coreference-resolution-based-entity/)** *(2013)* `measured` This journal article is the full description of the Stanford multi-pass sieve coreference system, which won the CoNLL-2011 shared task. 2. **[Stanford's Multi-Pass Sieve Coreference Resolution System at the CoNLL-2011 Shared Task](https://md.research.elamur.ai/free/en/papers/lee-2011-stanford-s-multi-pass-sieve/)** *(2011)* The system is a sequence of deterministic sieves, ordered from highest to lowest precision. Each sieve links clusters, not single mentions, and the clusters share attributes such as gender, number and animacy. The authors add a high-recall mention-detection sieve in front. Its exclusion rules remove pleonastic "it"… 3. **[A Multi-Pass Sieve for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/raghunathan-2010-multi-pass-sieve-coreference-resolution/)** *(2010)* `measured` The paper proposes an unsupervised, deterministic coreference system built as a sequence of "sieves": independent rule-based passes applied from the most precise to the least precise. 4. **[Parsing to Stanford Dependencies: Trade-offs between Speed and Accuracy](https://md.research.elamur.ai/free/en/papers/bank-2010-cer-parsing-stanford-dependencies-trade-offs/)** *(2010)* The authors compare ways of producing Stanford Dependencies quickly. One group is constituency parsers (Stanford, Charniak, Charniak-Johnson reranker, Bikel, Berkeley) followed by a conversion to dependencies; the other is dependency parsers trained directly on the basic dependencies (MaltParser's Nivre variants… ## Co-authors (11) - [Heeyoung Lee](https://md.research.elamur.ai/free/en/authors/heeyoung-lee/) ×3 - [Mihai Surdeanu](https://md.research.elamur.ai/free/en/authors/mihai-surdeanu/) ×3 - [Nathanael Chambers](https://md.research.elamur.ai/free/en/authors/nathanael-chambers/) ×3 - [Angel Chang](https://md.research.elamur.ai/free/en/authors/angel-chang/) ×2 - [Yves Peirsman](https://md.research.elamur.ai/free/en/authors/yves-peirsman/) ×2 - Chris Manning - [Christopher Manning](https://md.research.elamur.ai/free/en/authors/christopher-manning/) - Daniel Cer - Karthik Raghunathan - [Marie-Catherine de Marneffe](https://md.research.elamur.ai/free/en/authors/marie-catherine-de-marneffe/) - Sudarshan Rangarajan ## More papers in the library (11) Pages of the Research library: our summary, the ideas, references and who cites each. - [Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models](https://md.research.elamur.ai/free/en/papers/cheng-2023-marked-personas-natural-language-prompts/) — Myra Cheng, Esin Durmus, Dan Jurafsky · 2023 - [Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions](https://md.research.elamur.ai/free/en/papers/bianchi-2023-safety-tuned-llamas-lessons-improving/) — Federico Bianchi, Mirac Suzgun, Giuseppe Attanasio, et al. · 2023 - [Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference](https://md.research.elamur.ai/free/en/papers/held-2021-focus-what-matters-applying-discourse/) — William Held, Dan Iter, Dan Jurafsky · 2021 - [Social Bias Frames: Reasoning about Social and Power Implications of Language](https://md.research.elamur.ai/free/en/papers/sap-2020-social-bias-frames-reasoning-about/) — Maarten Sap, Saadia Gabriel, Lianhui Qin, et al. · 2020 - [Neural Net Models of Open-domain Discourse Coherence](https://md.research.elamur.ai/free/en/papers/li-2017-neural-net-models-open-domain/) — Jiwei Li, Dan Jurafsky · 2017 - [Deep Reinforcement Learning for Dialogue Generation](https://md.research.elamur.ai/free/en/papers/li-2016-deep-reinforcement-learning-dialogue-generation/) — Jiwei Li, Will Monroe, Alan Ritter, et al. · 2016 - [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://md.research.elamur.ai/free/en/papers/hamilton-2016-diachronic-word-embeddings-reveal-statistical/) — William L. Hamilton, Jure Leskovec, Dan Jurafsky · 2016 - [Visualizing and Understanding Neural Models in NLP](https://md.research.elamur.ai/free/en/papers/li-2016-visualizing-understanding-neural-models-nlp/) — Jiwei Li, Xinlei Chen, Eduard Hovy, et al. · 2016 - [A computational approach to politeness with application to social factors](https://md.research.elamur.ai/free/en/papers/mizil-2013-computational-approach-politeness-application-social/) — Cristian Danescu-Niculescu-Mizil, Moritz Sudhof, Dan Jurafsky, et al. · 2013 - [Implicatures and Nested Beliefs in Approximate Decentralized-POMDPs](https://md.research.elamur.ai/free/en/papers/vogel-2013-implicatures-nested-beliefs-approximate-decentralized/) — Adam Vogel, Christopher Potts, Dan Jurafsky · 2013 - [Joint Entity and Event Coreference Resolution across Documents](https://md.research.elamur.ai/free/en/papers/lee-2012-joint-entity-event-coreference-resolution/) — Heeyoung Lee, Marta Recasens, Angel Chang, et al. · 2012