--- title: "Noun Phrase Coreference as Clustering — Cardie 1999" description: "Cardie and Wagstaff address noun phrase coreference resolution, which means grouping the noun phrases in a text that refer to the same real-world entity." authors: ["Claire Cardie", "Kiri Wagstaff"] year: 1999 venue: "1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora" source: "https://aclanthology.org/W99-0611/" licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)" licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/papers/cardie-1999-noun-phrase-coreference-as-clustering/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/cardie-1999-noun-phrase-coreference-as-clustering/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [1999](https://md.research.elamur.ai/free/papers/1999/) / Noun Phrase Coreference as Clustering # Noun Phrase Coreference as Clustering **Authors:** [Claire Cardie](https://md.research.elamur.ai/free/authors/claire-cardie/), Kiri Wagstaff · **Year:** 1999 · **Venue:** 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/) **Links:** [Publisher's page](https://aclanthology.org/W99-0611/) · [PDF](https://aclanthology.org/W99-0611.pdf) · [OpenAlex](https://openalex.org/W3195375) **Citations:** 156 citations (openalex-snapshot · 2026-10-14) · [42](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Cardie and Wagstaff address noun phrase coreference resolution, which means grouping the noun phrases in a text that refer to the same real-world entity. Most systems of the time relied on hand-written rules, and the few learning-based ones were supervised pairwise classifiers. The authors instead treat coreference as unsupervised clustering. Base noun phrases are found with the Empire NP finder, and each one becomes an 11-feature vector built with simple heuristics and WordNet. A weighted distance metric combines per-feature incompatibility scores: - **Infinite weights** act as hard filters. - **Negative-infinite weights** force a merge. - **Weights equal to the clustering radius r** express soft preferences that other evidence can override. A greedy clustering procedure then links each NP to compatible earlier NPs that lie within r. On the MUC-6 coreference corpus, the method reaches an F-measure of 53.6%. This sits between the weakest and strongest MUC-6 systems and beats RESOLVE, the only learning-based participant. The result matters because it needs no annotated training data or full parse, and new linguistic knowledge can be added simply by extending the metric. ## Key points - **Results on MUC-6:** the clustering approach scores F = 53.6%. For comparison, RESOLVE (decision trees) reaches 47%, the best MUC-6 system 65%, and the worst 40%. - **Features:** each base NP is described by 11 automatically derived features: - words and head noun - position - pronoun type and article - appositive status, number and proper-name status - semantic class and gender (both from WordNet) - animacy - **Hard constraints:** number, proper name, semantic class, gender and animacy work as hard filters. Appositive and words-substring terms force coreference. - **Tuning and preprocessing:** only the radius r and some weights are tuned on development data. The method needs neither a full syntactic parse nor a large statistics corpus. - **Limitations and future work:** errors come partly from greedy linking that can never be undone. The authors propose less greedy alternatives, such as linking only to the first or best-ranked antecedent, or processing links in ranked order. They also plan to learn the weights automatically and to use cues from centering theory. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/) Topics: [Coreference resolution](https://md.research.elamur.ai/free/topics/coreference-resolution/) ## Cited by Cited by 42 papers of our library cite it (of the 140673 whose bibliographies we read); 16 of them have a page here, 26 not yet. - [Unsupervised Event Coreference Resolution](https://md.research.elamur.ai/free/papers/bejan-2014-unsupervised-event-coreference-resolution/) - [EM Works for Pronoun Anaphora Resolution](https://md.research.elamur.ai/free/papers/charniak-2009-em-works-pronoun-anaphora-resolution/) - [An Expectation Maximization Approach to Pronoun Resolution](https://md.research.elamur.ai/free/papers/cherry-2005-expectation-maximization-approach-pronoun-resolution/) - [Unsupervised Coreference Resolution in a Nonparametric Bayesian Model](https://md.research.elamur.ai/free/papers/haghighi-2007-unsupervised-coreference-resolution-nonparametric-bayesian/) - [Multilingual Coreference Resolution](https://md.research.elamur.ai/free/papers/harabagiu-2000-multilingual-coreference-resolution/) - [Deterministic Coreference Resolution Based on Entity-Centric, Precision-Ranked Rules](https://md.research.elamur.ai/free/papers/lee-2013-deterministic-coreference-resolution-based-entity/) - [Automatic Pronominal Anaphora Resolution in English Texts](https://md.research.elamur.ai/free/papers/liang-2003-automatic-pronominal-anaphora-resolution-english/) - [Multigraph Clustering for Unsupervised Coreference Resolution](https://md.research.elamur.ai/free/papers/martschat-2013-multigraph-clustering-unsupervised-coreference-resolution/) - [Combining Sample Selection and Error-Driven Pruning for Machine Learning of Coreference Rules](https://md.research.elamur.ai/free/papers/ng-2002-combining-sample-selection-error-driven/) - [Machine Learning for Coreference Resolution: From Local Classification to Global Ranking](https://md.research.elamur.ai/free/papers/ng-2005-machine-learning-coreference-resolution-local/) - [Supervised Noun Phrase Coreference Research: The First Fifteen Years](https://md.research.elamur.ai/free/papers/ng-2010-supervised-noun-phrase-coreference-research/) - [A Constraint-Based Hypergraph Partitioning Approach to Coreference Resolution](https://md.research.elamur.ai/free/papers/sapena-2013-constraint-based-hypergraph-partitioning-approach/) - [A Machine Learning Approach to Coreference Resolution of Noun Phrases](https://md.research.elamur.ai/free/papers/soon-2001-machine-learning-approach-coreference-resolution/) - [Coreference Resolution with and without Linguistic Knowledge](https://md.research.elamur.ai/free/papers/uryupina-2006-coreference-resolution-without-linguistic-knowledge/) - [Antecedent Selection Techniques for High-Recall Coreference Resolution](https://md.research.elamur.ai/free/papers/versley-2007-antecedent-selection-techniques-high-recall/) - [An NP-Cluster Based Approach to Coreference Resolution](https://md.research.elamur.ai/free/papers/yang-2004-np-cluster-based-approach-coreference/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{cardie1999noun, title = {{Noun Phrase Coreference as Clustering}}, author = {Cardie, Claire and Wagstaff, Kiri}, booktitle = {1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora}, year = {1999}, url = {https://aclanthology.org/W99-0611/} } ``` - [← previous Interpreting and Generating Indirect Answers](https://md.research.elamur.ai/free/papers/green-1999-interpreting-generating-indirect-answers/) - [next → Resolving Discourse Deictic Anaphora in Dialogues](https://md.research.elamur.ai/free/papers/prag-1999-eckert-resolving-discourse-deictic-anaphora-dialogues/) - [↑ back to papers of 1999](https://md.research.elamur.ai/free/papers/1999/) ## References 13 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Chinatsu Aone and William Bennett. 1995. 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