--- title: "Corpus-Based Identification of Non-Anaphoric — Bean 1999" description: "Coreference resolvers usually look for an antecedent for every definite noun phrase." authors: ["David L. Bean", "Ellen Riloff"] year: 1999 venue: "Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics" source: "https://aclanthology.org/P99-1048/" doi: "10.3115/1034678.1034737" 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/bean-1999-corpus-based-identification-non-anaphoric/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/bean-1999-corpus-based-identification-non-anaphoric/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [1999](https://md.research.elamur.ai/free/papers/1999/) / Corpus-Based Identification of Non-Anaphoric Noun Phrases # Corpus-Based Identification of Non-Anaphoric Noun Phrases **Authors:** David L. Bean, [Ellen Riloff](https://md.research.elamur.ai/free/authors/ellen-riloff/) · **Year:** 1999 · **Venue:** Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics · **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/P99-1048/) · [PDF](https://aclanthology.org/P99-1048.pdf) · [DOI 10.3115/1034678.1034737](https://doi.org/10.3115/1034678.1034737) · [OpenAlex](https://openalex.org/W2107232048) **Citations:** 66 citations (openalex-snapshot · 2026-10-14) · [23](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Coreference resolvers usually look for an antecedent for every definite noun phrase. Many such NPs, which the authors call "existential", need none: world knowledge or their own modifiers already make the referent clear, as with "the White House". Bean and Riloff build an automatic, corpus-driven filter that flags these non-anaphoric definite NPs before resolution. It needs no hand-coded lists and no full parsing. Syntactic heuristics are combined with three resources learned from 1,600 MUC-4 terrorism articles: - definite NPs from first sentences (S1) - generalized Existential Head Patterns (EHP) - a Definite-Only (DO) list of NPs that practically never appear with indefinite determiners A "vaccine" step uses definiteness probabilities to discard unreliable S1/EHP entries. On 50 hand-annotated test texts, the tuned system reached 78% recall and 87% precision, slightly beating the earlier Vieira & Poesio system. Precision matters most for a pre-filter, and about two thirds of test definite NPs were independent existentials. The method could therefore make coreference resolution faster and more accurate while staying portable across domains. ## Key points - The test set had 1,001 definite NPs: - 48% syntactically independent existential - 15% semantically independent existential - 9% associative existential - 28% referential - The baseline (label everything existential) gives 100% recall and 72% precision. Syntactic heuristics alone (seven rule-in, two rule-out) reach 43% recall at roughly 92–93% precision. - Training produced 297 EHPs and a DO list of 65 head nouns plus 321 full NPs. The DO thresholds were at least 5 occurrences, with 100% definite use for head nouns and 75% for full NPs. - All methods combined (heuristics + S1 + EHP + DO) gave 81.7% recall and 82.2% precision. Vaccine Va (upper threshold 70%, lower 25%) moved this to 77.7% recall and 86.6% precision. Vb (upper 50%) gave 79.1% recall and 84.5% precision. - Vieira & Poesio reported 74% recall and 85% precision. Scored only on independent existentials, this system reaches 81.8% recall and 85.6% precision with Va. *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 23 papers of our library cite it (of the 140673 whose bibliographies we read); 12 of them have a page here, 11 not yet. - [Unsupervised Learning of Contextual Role Knowledge for Coreference Resolution](https://md.research.elamur.ai/free/papers/bean-2004-unsupervised-learning-contextual-role-knowledge/) - [Coreference-inspired Coherence Modeling](https://md.research.elamur.ai/free/papers/elsner-2008-coreference-inspired-coherence-modeling/) - [Using Word Similarity Lists for Resolving Indirect Anaphora](https://md.research.elamur.ai/free/papers/gasperin-2004-word-similarity-lists-resolving-indirect/) - [Multilingual Coreference Resolution](https://md.research.elamur.ai/free/papers/harabagiu-2000-multilingual-coreference-resolution/) - [Dependency-driven Anaphoricity Determination for Coreference Resolution](https://md.research.elamur.ai/free/papers/kong-2010-dependency-driven-anaphoricity-determination-coreference/) - [Coreference or Not: A Twin Model for Coreference Resolution](https://md.research.elamur.ai/free/papers/luo-2007-coreference-or-not-twin-model/) - [Learning Noun Phrase Anaphoricity to Improve Conference Resolution: Issues in Representation and Optimization](https://md.research.elamur.ai/free/papers/ng-2004-learning-noun-phrase-anaphoricity-improve/) - [Graph-Cut-Based Anaphoricity Determination for Coreference Resolution](https://md.research.elamur.ai/free/papers/ng-2009-graph-cut-based-anaphoricity-determination/) - [Supervised Noun Phrase Coreference Research: The First Fifteen Years](https://md.research.elamur.ai/free/papers/ng-2010-supervised-noun-phrase-coreference-research/) - [Supervised Models for Coreference Resolution](https://md.research.elamur.ai/free/papers/rahman-2009-supervised-models-coreference-resolution/) - [Coreference Resolution Using Competition Learning Approach](https://md.research.elamur.ai/free/papers/yang-2003-coreference-resolution-competition-learning-approach/) - [Global Learning of Noun Phrase Anaphoricity in Coreference Resolution via Label Propagation](https://md.research.elamur.ai/free/papers/zhou-2009-global-learning-noun-phrase-anaphoricity/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{bean1999corpus, title = {{Corpus-Based Identification of Non-Anaphoric Noun Phrases}}, author = {Bean, David L. and Riloff, Ellen}, booktitle = {Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics}, year = {1999}, doi = {10.3115/1034678.1034737}, url = {https://doi.org/10.3115/1034678.1034737} } ``` - [← previous Corpus Annotation for Parser Evaluation](https://md.research.elamur.ai/free/papers/enud-1999-carroll-corpus-annotation-parser-evaluation/) - [next → Cross-Document Event Coreference: Annotations, Experiments, and Observations](https://md.research.elamur.ai/free/papers/bagga-1999-cross-document-event-coreference-annotations/) - [↑ back to papers of 1999](https://md.research.elamur.ai/free/papers/1999/) ## References 10 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. James Allen. 1995. Natural Language Understanding. Benjamin/Cummings Press, Redwood City, CA. [find](https://scholar.google.com/scholar?q=James+Allen%2E+1995%2E+Natural+Language+Understanding%2E+Benjamin%2FCummings+Press%2C+Redwood+City%2C+CA%2E) 2. Chinatsu Aone and Scott William Bennett. 1996. Applying Machine Learning to Anaphora Resolution. In Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Understanding, pages 302-314. Springer-Verlag, Berlin. [find](https://scholar.google.com/scholar?q=Chinatsu+Aone+and+Scott+William+Bennett%2E+1996%2E+Applying+Machine+Learning+to+Anaphora+Resolution%2E+In+Connectionist%2C+Statistical%2C+and+Symbolic+Approaches+to+Learning+for+Natural+Language+Understanding%2C+) 3. Andrew Kehler. 1997. Probabilistic coreference in information extraction. In Proceedings of the Second Conference on Empirical Methods in Natural Language Processing (EMNLP-97). 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An algorithm for pronomial anaphora resolution. Computational Linguistics, 20(4):535-561. [find](https://scholar.google.com/scholar?q=Shalom+Lappin+and+Herbert+J%2E+Leass%2E+1994%2E+An+algorithm+for+pronomial+anaphora+resolution%2E+Computational+Linguistics%2C+20%284%29%3A535%2D561%2E) 6. Joseph F. McCarthy and Wendy G. Lehnert. 1995. Using Decision Trees for Coreference Resolution. [find](https://scholar.google.com/scholar?q=Joseph+F%2E+McCarthy+and+Wendy+G%2E+Lehnert%2E+1995%2E+Using+Decision+Trees+for+Coreference+Resolution%2E) 7. In Proceedings of the l~th International Joint Conference on Artificial Intelligence (IJCAI-95), pages 1050-1055. [find](https://scholar.google.com/scholar?q=In+Proceedings+of+the+l%7Eth+International+Joint+Conference+on+Artificial+Intelligence+%28IJCAI%2D95%29%2C+pages+1050%2D1055%2E) 8. Ellen F. Prince. 1981. Toward a taxonomy of given-new information. In Peter Cole, editor, Radical Pragmatics, pages 223-255. Academic Press. 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