Claire Cardie · page 2
Papers
- Coreference Resolution with 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… - Partially Supervised Coreference Resolution for Opinion Summarization through Structured Rule Learning (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… - OpinionFinder: A System for 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. - Combining Sample Selection and Error-Driven Pruning for Machine Learning of Coreference Rules (2002 · 66 citations)
The paper addresses noun phrase coreference resolution, which is usually treated as pairwise classification of NPs followed by clustering. - Bootstrapping Coreference Classifiers with Multiple Machine Learning Algorithms (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… - The Role of Pragmatic and Discourse Context in Determining Argument Impact (2019 · 24 citations)
This work asks whether the impact of an argumentative claim depends on the discussion that comes before it. - Better LLM Reasoning via 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.
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