--- title: "Claire Cardie — papers, page 2 — Elamur Research" description: "Claire Cardie, Cornell University: 32 papers on Elamur Research (1999–2026), h-index 75 — affiliations, topics, co-authors and who cites…" canonical: "https://research.elamur.ai/free/en/authors/claire-cardie/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/claire-cardie/page/2/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Claire Cardie](https://md.research.elamur.ai/free/en/authors/claire-cardie/) / page 2 # Claire Cardie · page 2 32 papers in our library · 1999–2026 · page 2 of 2 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/claire-cardie/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/claire-cardie/) [1](https://md.research.elamur.ai/free/en/authors/claire-cardie/) **2** 26. **[Coreference Resolution with Reconcile](https://md.research.elamur.ai/free/en/papers/stoyanov-2010-coreference-resolution-reconcile/)** *(2010 · 114 citations)* Results for noun phrase coreference resolution are hard to compare across systems. Building a complete pipeline is costly, so many studies replace subtasks such as mention detection, anaphoricity determination, named entity recognition or semantic class identification with gold-standard annotations. Benchmarks also… 27. **[Partially Supervised Coreference Resolution for Opinion Summarization through Structured Rule Learning](https://md.research.elamur.ai/free/en/papers/stoyanov-2006-partially-supervised-coreference-resolution-opinion/)** *(2006 · 90 citations)* Opinion summaries need every opinion grouped under the real-world entity that holds it. The authors frame this as source coreference resolution: deciding which opinion-source mentions in a document point to the same entity. Standard NP coreference systems expect coreference labels for every noun phrase. Opinion… 28. **[OpinionFinder: A System for Subjectivity Analysis](https://md.research.elamur.ai/free/en/papers/wilson-2005-opinionfinder-system-subjectivity-analysis/)** *(2005 · 78 citations)* OpinionFinder is a demonstration system that automatically detects subjective language in English text, meaning opinions, emotions, speculations and other private states. 29. **[Combining Sample Selection and Error-Driven Pruning for Machine Learning of Coreference Rules](https://md.research.elamur.ai/free/en/papers/ng-2002-combining-sample-selection-error-driven/)** *(2002 · 66 citations)* The paper addresses noun phrase coreference resolution, which is usually treated as pairwise classification of NPs followed by clustering. 30. **[Bootstrapping Coreference Classifiers with Multiple Machine Learning Algorithms](https://md.research.elamur.ai/free/en/papers/ng-2003-bootstrapping-coreference-classifiers-multiple-machine/)** *(2003 · 47 citations)* Co-training normally needs two feature "views" that are each sufficient and conditionally independent. Noun phrase coreference resolution has no natural split of this kind, so Ng and Cardie propose a single-view bootstrapping method. In place of two views, it uses two different learners: a generative naive Bayes… 31. **[The Role of Pragmatic and Discourse Context in Determining Argument Impact](https://md.research.elamur.ai/free/en/papers/durmus-2019-role-pragmatic-discourse-context-determining/)** *(2019 · 24 citations)* This work asks whether the impact of an argumentative claim depends on the discussion that comes before it. 32. **[Better LLM Reasoning via Dual-Play](https://md.research.elamur.ai/free/en/papers/zhang-2026-better-llm-reasoning-dual-play/)** *(2026 · 3 citations)* Reinforcement learning with verifiable rewards (RLVR) has improved LLM reasoning, but it depends on large sets of curated problems with correct answers. [← prev](https://md.research.elamur.ai/free/en/authors/claire-cardie/) [1](https://md.research.elamur.ai/free/en/authors/claire-cardie/) **2**