--- title: "Who is Who and What is What — Baron 2008" description: "Linking mentions of the same real-world person or organization across many documents involves two problems." authors: ["Alex Baron", "Marjorie Freedman"] year: 2008 venue: "Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing" source: "https://aclanthology.org/D08-1029/" 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/baron-2008-who-is-who-what-is/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/baron-2008-who-is-who-what-is/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2008](https://md.research.elamur.ai/free/papers/2008/) / Who is Who and What is What: Experiments in Cross-Document Co-Reference # Who is Who and What is What: Experiments in Cross-Document Co-Reference **Authors:** Alex Baron, Marjorie Freedman · **Year:** 2008 · **Venue:** Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing · **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/D08-1029/) · [PDF](https://aclanthology.org/D08-1029.pdf) **Citations:** [13](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Linking mentions of the same real-world person or organization across many documents involves two problems. One entity may appear under several names, such as Mahmoud Abbas and Abu Mazen. One name may also belong to several different entities. Baron and Freedman (BBN) present a language-independent system that handles both problems. They evaluated it in the ACE 2008 global entity task on English and Arabic collections of about 10K documents each. The SERIF information extraction engine first detects document-level entities. A name variation module then scores pairs of corpus name strings using aliases, string similarity and translation resources. For each name and its accepted variants, a complete-link agglomerative clustering step separates the candidate entities. Because training data was scarce, features are combined with manually tuned heuristics. The system beats an exact-match baseline in both languages, and the gains are larger on entities that occur in several documents. Clustering small subsets keeps runtime manageable. Feeding cross-document results back also improved within-document extraction. This suggests that corpus-level knowledge can strengthen document-level IE. ## Key points - **Results:** - English: 8.4% relative error reduction over exact name match, rising to 14.2% for entities seen in more than one document. - Arabic: 7% reduction, rising to 12.4% on the same subset. - **Evaluation data:** ACE 2008 GEDR covered named persons and organizations, with over 10K documents per language and roughly 400 annotated reference documents per language. - **Name variation features:** - web-mined and corpus-mined aliases; - character edit distance; - token subset trees; - SERIF relation and coreference statistics; - translation dictionaries drawn from MT training data and Wikipedia cross-language links. - **Scalability on English:** - about 135K document entities and 62,516 unique name strings, each starting one clustering instance; - 36% of instances were skipped, and 99.7% of runs finished in under 1 second; - the largest instance ("Xinhua", 1848 entities) took 2.6 hours. - **Persons vs. organizations:** organizations scored noticeably worse than persons. In the name-only local task, ORG B3 was 82.6 versus 90.1 for PER, partly because the name-versus-nominal distinction is fuzzier for organizations. *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 13 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 11 not yet. - [Cross-document coreference: An approach to capturing coreference without context](https://md.research.elamur.ai/free/papers/bettner-2019-cross-document-coreference-approach-capturing/) - [Large-Scale Cross-Document Coreference Using Distributed Inference and Hierarchical Models](https://md.research.elamur.ai/free/papers/singh-2011-large-scale-cross-document-coreference/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{baron2008who, title = {{Who is Who and What is What: Experiments in Cross-Document Co-Reference}}, author = {Baron, Alex and Freedman, Marjorie}, booktitle = {Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing}, year = {2008}, url = {https://aclanthology.org/D08-1029/} } ``` - [← previous VARD 2: A tool for dealing with spelling variation in historical corpora](https://md.research.elamur.ai/free/papers/enprag-2008-baron-vard2-tool-dealing-spelling-variation/) - [next → Word-Based or Morpheme-Based? Annotation Strategies for Modern Hebrew Clitics](https://md.research.elamur.ai/free/papers/lingbulk-2008-tsarfaty-word-based-or-morpheme-based/) - [↑ back to papers of 2008](https://md.research.elamur.ai/free/papers/2008/) ## References 15 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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Algorithms for Scoring Coreference Chains. In Proceedings of the Linguistic Coreference Workshop at the First International Conference on Language Resources and Evaluation (LREC'98), pages 563-566. [find](https://scholar.google.com/scholar?q=Bagga%2C+Amit+%26+Breck+Baldwin%2E+1998a%2E+Algorithms+for+Scoring+Coreference+Chains%2E+In+Proceedings+of+the+Linguistic+Coreference+Workshop+at+the+First+International+Conference+on+Language+Resources+and+Eva) [our card](https://md.research.elamur.ai/free/papers/bagga-1998-algorithms-scoring-coreference-chains/) 5. Bagga, Amit & Breck Baldwin. 1998b. Entity-Based Cross-Document Coreferencing Using the Vector Space Model. In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and the 17th International Conference on Computational Linguistics (COLING-ACL’98), pages 79-85. 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