--- title: "Entity-Based Cross-Document Coreferencing Using — Bagga 1998" description: "The paper addresses cross-document coreference: deciding whether mentions of the same name in different texts refer to one real-world entity." authors: ["Amit Bagga", "Breck Baldwin"] year: 1998 venue: "36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1" source: "https://aclanthology.org/P98-1012/" doi: "10.3115/980845.980859" 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/en/papers/bagga-1998-entity-based-cross-document-coreferencing/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/bagga-1998-entity-based-cross-document-coreferencing/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [1998](https://md.research.elamur.ai/free/en/papers/1998/) / Entity-Based Cross-Document Coreferencing Using the Vector Space Model # Entity-Based Cross-Document Coreferencing Using the Vector Space Model **Authors:** [Amit Bagga](https://md.research.elamur.ai/free/en/authors/amit-bagga/), [Breck Baldwin](https://md.research.elamur.ai/free/en/authors/breck-baldwin/) · **Year:** 1998 · **Venue:** 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1 · **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/P98-1012/) · [PDF](https://aclanthology.org/P98-1012.pdf) · [DOI 10.3115/980845.980859](https://doi.org/10.3115/980845.980859) · [OpenAlex](https://openalex.org/W2113227740) **Citations:** 533 citations (openalex-snapshot · 2026-10-14) · [166](#cited-by) in our library, of the 140884 papers whose bibliographies we read ## Summary The paper addresses cross-document coreference: deciding whether mentions of the same name in different texts refer to one real-world entity. The focus is on people who share a name, a case that named-entity tools such as NetOwl and Textract cannot separate. The pipeline starts with CAMP, Penn's within-document coreference system. For each article, a SentenceExtractor gathers every sentence that mentions the chosen entity's coreference chain, which yields an entity-focused summary. VSM-Disambiguate then compares summaries pairwise using a standard tf-idf vector space model with cosine normalization, and merges pairs whose similarity exceeds a threshold. The test set is 197 New York Times articles matching a "John Smith" pattern. The authors also propose B-CUBED, a per-mention scoring method, because the MUC link-based scorer rewards bad merges too generously in this setting. The best F-measure is 84.6%. A same-name baseline gets only 23% precision under B-CUBED. The work matters for multi-document summarization, information fusion and tracking entities across collections. ## Key points - The data is 197 NYT articles from 1996–1997, all matching the regex `/John.*?Smith/`. They mention 35 distinct John Smiths: 24 appear in a single article, and the other 173 articles cover 11 people. No NYT data was used for training. - The architecture is CAMP (a top system in MUC-6/MUC-7) → SentenceExtractor → VSM-Disambiguate. Similarity is the sum of products of tf × log(N/df) weights over shared stemmed, stop-word-filtered terms, normalized by vector length. - The best B-CUBED F-measure is 84.6% when the term space is built from the extracted summaries only. It drops to 78.0% when the space is built from the full articles. - Treating all John Smiths as one person scores 23% precision and 100% recall under B-CUBED. Under the MUC scorer the same baseline gets 83% precision. - In a worked example, the MUC scorer gives 90% precision to two responses of clearly different quality. B-CUBED separates them, giving 58% and 76%. ## Interesting ideas - **Reuse within-document coreference to build entity profiles.** Resolving pronouns and short name forms first lets you collect facts about an entity that would otherwise be missed, such as a job title stated only in a sentence with "he". This applies to any entity linking or profile-building task, including morphologically rich languages. - **Query-focused summaries instead of whole documents.** Comparing only the sentences about the target entity reduces noise from unrelated content. Here it gave a clear F-measure gain, which suggests that narrowing the context can beat richer input. - **Turn the cross-document problem into a within-document one for evaluation.** Treating each document as a mention inside a "meta document" lets existing coreference scorers run unchanged. This is a cheap way to bootstrap evaluation for new clustering-style tasks. - **Score per element, not per link.** B-CUBED computes precision and recall for each item and averages them. As a result, wrongly merging two large clusters costs more than attaching a small one. This is useful for evaluating any clustering, deduplication or record-linkage system where error sizes differ. - **A deliberately ambiguous stress-test set.** Selecting documents by a single very common name with a simple regex gives a hard and cheap benchmark for disambiguation. The same idea could be applied to frequent names or toponyms in other languages. *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: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) Topics: [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) ## Cited by Cited by 166 papers of our library cite it (of the 140884 whose bibliographies we read); 26 of them have a page here, 140 not yet. - [Evaluation of named entity coreference](https://md.research.elamur.ai/free/en/papers/agarwal-2019-evaluation-named-entity-coreference/) - [Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/anikina-2025-reverse-probing-evaluating-knowledge-transfer/) - [Using Coreference Chains for Text Summarization](https://md.research.elamur.ai/free/en/papers/azzam-1999-coreference-chains-text-summarization/) - [Cross-Document Event Coreference: Annotations, Experiments, and Observations](https://md.research.elamur.ai/free/en/papers/bagga-1999-cross-document-event-coreference-annotations/) - [Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution](https://md.research.elamur.ai/free/en/papers/barhom-2019-revisiting-joint-modeling-cross-document/) - [Who is Who and What is What: Experiments in Cross-Document Co-Reference](https://md.research.elamur.ai/free/en/papers/baron-2008-who-is-who-what-is/) - [Event Coreference Data (Almost) for Free: Mining Hyperlinks from Online News](https://md.research.elamur.ai/free/en/papers/bugert-2021-event-coreference-data-almost-free/) - [Realistic Evaluation Principles for Cross-document Coreference Resolution](https://md.research.elamur.ai/free/en/papers/cattan-2021-realistic-evaluation-principles-cross-document/) - [Beyond Word Boundaries: A Hebrew Coreference Benchmark and an Evaluation Protocol for Morphologically Complex Text](https://md.research.elamur.ai/free/en/papers/coref-2026-greenfeld-beyond-word-boundaries-hebrew-coreference/) - [A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference Resolution](https://md.research.elamur.ai/free/en/papers/ding-2024-rationale-centric-counterfactual-data-augmentation/) - [Cross-Document Co-Reference Resolution using Sample-Based Clustering with Knowledge Enrichment](https://md.research.elamur.ai/free/en/papers/dutta-2015-cross-document-co-reference-resolution/) - [Cross-Document Coreference on a Large Scale Corpus](https://md.research.elamur.ai/free/en/papers/gooi-2004-cross-document-coreference-large-scale/) - [Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends](https://md.research.elamur.ai/free/en/papers/martinelli-2024-maverick-efficient-accurate-coreference-resolution/) - [Machine Learning for Coreference Resolution: From Local Classification to Global Ranking](https://md.research.elamur.ai/free/en/papers/ng-2005-machine-learning-coreference-resolution-local/) - [LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/otmazgin-2023-lingmess-linguistically-informed-multi-expert/) - [Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective](https://md.research.elamur.ai/free/en/papers/porada-2024-challenges-evaluating-generalization-coreference-resolution/) - [Supervised Models for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/rahman-2009-supervised-models-coreference-resolution/) - [Translation-Based Projection for Multilingual Coreference Resolution](https://md.research.elamur.ai/free/en/papers/rahman-2012-translation-based-projection-multilingual-coreference/) - [Streaming Cross Document Entity Coreference Resolution](https://md.research.elamur.ai/free/en/papers/rao-2010-streaming-cross-document-entity-coreference/) - [Large-Scale Cross-Document Coreference Using Distributed Inference and Hierarchical Models](https://md.research.elamur.ai/free/en/papers/singh-2011-large-scale-cross-document-coreference/) - [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/) - [Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution](https://md.research.elamur.ai/free/en/papers/tran-2021-exploiting-document-structures-cluster-consistencies/) - [Deep Cross-Lingual Coreference Resolution for Less-Resourced Languages: The Case of Basque](https://md.research.elamur.ai/free/en/papers/urbizu-2019-deep-cross-lingual-coreference-resolution/) - [MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction](https://md.research.elamur.ai/free/en/papers/wang-2022-maven-ere-unified-large-scale/) - [An NP-Cluster Based Approach to Coreference Resolution](https://md.research.elamur.ai/free/en/papers/yang-2004-np-cluster-based-approach-coreference/) - [Domain Adaptation for Coreference Resolution: An Adaptive Ensemble Approach](https://md.research.elamur.ai/free/en/papers/yang-2012-domain-adaptation-coreference-resolution-adaptive/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{bagga1998entity, title = {{Entity-Based Cross-Document Coreferencing Using the Vector Space Model}}, author = {Bagga, Amit and Baldwin, Breck}, booktitle = {36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1}, year = {1998}, doi = {10.3115/980845.980859}, url = {https://doi.org/10.3115/980845.980859} } ``` - [← previous Dynamic Coreference-Based Summarization](https://md.research.elamur.ai/free/en/papers/baldwin-1998-dynamic-coreference-based-summarization/) - [next → Evaluating a Focus-Based Approach to Anaphora Resolution](https://md.research.elamur.ai/free/en/papers/azzam-1998-evaluating-focus-based-approach-anaphora/) - [↑ back to papers of 1998](https://md.research.elamur.ai/free/en/papers/1998/) ## References 7 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Bagga, Amit, and Breck Baldwin. Algorithms for Scoring Coreference Chains. To appear at The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference, May 1998. [find](https://scholar.google.com/scholar?q=Bagga%2C+Amit%2C+and+Breck+Baldwin%2E+Algorithms+for+Scoring+Coreference+Chains%2E+To+appear+at+The+First+International+Conference+on+Language+Resources+and+Evaluation+Workshop+on+Linguistics+Coreference%2C+May) [our card](https://md.research.elamur.ai/free/en/papers/bagga-1998-algorithms-scoring-coreference-chains/) 2. Bagga, Amit, and Breck Baldwin. How Much Processing Is Required for Cross-Document Coreference? To appear at The First International Con-, ferenee on Language Resources and Evaluation on Linguistics Coreferenee, May 1998. [find](https://scholar.google.com/scholar?q=Bagga%2C+Amit%2C+and+Breck+Baldwin%2E+How+Much+Processing+Is+Required+for+Cross%2DDocument+Coreference%3F+To+appear+at+The+First+International+Con%2D%2C+ferenee+on+Language+Resources+and+Evaluation+on+Linguistics+C) 3. Baldwin, Breck, et el. University of Pennsylvania: Description of the University of Pennsylvania System Used for MUC-6, Proceedings of the Sixth Message Understanding Conference (MUC-6), pp. 177-191, November 1995. [find](https://scholar.google.com/scholar?q=Baldwin%2C+Breck%2C+et+el%2E+University+of+Pennsylvania%3A+Description+of+the+University+of+Pennsylvania+System+Used+for+MUC%2D6%2C+Proceedings+of+the+Sixth+Message+Understanding+Conference+%28MUC%2D6%29%2C+pp%2E+177%2D191%2C+) 4. Grishman, Ralph. Whither Written Language Evaluation?, Proceedings of the Human Language Technology Workshop, pp. 120-125, March 1994, San Francisco: Morgan Kaufmann. [find](https://scholar.google.com/scholar?q=Grishman%2C+Ralph%2E+Whither+Written+Language+Evaluation%3F%2C+Proceedings+of+the+Human+Language+Technology+Workshop%2C+pp%2E+120%2D125%2C+March+1994%2C+San+Francisco%3A+Morgan+Kaufmann%2E) 5. Proceedings of the Seventh Message Understanding Conference (MUC-7), April 1998. [find](https://scholar.google.com/scholar?q=Proceedings+of+the+Seventh+Message+Understanding+Conference+%28MUC%2D7%29%2C+April+1998%2E) 6. Salton, Gerard. Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer, 1989, Reading, MA: Addison-Wesley. [find](https://scholar.google.com/scholar?q=Salton%2C+Gerard%2E+Automatic+Text+Processing%3A+The+Transformation%2C+Analysis%2C+and+Retrieval+of+Information+by+Computer%2C+1989%2C+Reading%2C+MA%3A+Addison%2DWesley%2E) 7. Vilain, Marc, et el. A Model-Theoretic Coreference Scoring Scheme, Proceedings of the Sixth Message Understanding Conference (MUC-6), pp. 45-52, November 1995, San Francisco: Morgan Kaufmann. [find](https://scholar.google.com/scholar?q=Vilain%2C+Marc%2C+et+el%2E+A+Model%2DTheoretic+Coreference+Scoring+Scheme%2C+Proceedings+of+the+Sixth+Message+Understanding+Conference+%28MUC%2D6%29%2C+pp%2E+45%2D52%2C+November+1995%2C+San+Francisco%3A+Morgan+Kaufmann%2E) [our card](https://md.research.elamur.ai/free/en/papers/vilain-1995-model-theoretic-coreference-scoring-scheme/)