--- title: "Using the Web in Machine Learning for — Modjeska 2003" description: "The paper addresses other-anaphors: noun phrases modified by \"other\" or \"another\" whose antecedents must be found in the preceding discourse rather than…" authors: ["Natalia N. Modjeska", "Katja Markert", "Malvina Nissim"] year: 2003 venue: "Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing" source: "https://aclanthology.org/W03-1023/" 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/modjeska-2003-web-machine-learning-other-anaphora/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/modjeska-2003-web-machine-learning-other-anaphora/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2003](https://md.research.elamur.ai/free/papers/2003/) / Using the Web in Machine Learning for Other-Anaphora Resolution # Using the Web in Machine Learning for Other-Anaphora Resolution **Authors:** Natalia N. Modjeska, [Katja Markert](https://md.research.elamur.ai/free/authors/katja-markert/), [Malvina Nissim](https://md.research.elamur.ai/free/authors/malvina-nissim/) · **Year:** 2003 · **Venue:** Proceedings of the 2003 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/W03-1023/) · [PDF](https://aclanthology.org/W03-1023.pdf) · [OpenAlex](https://openalex.org/W2083783479) **Citations:** 53 citations (openalex-snapshot · 2026-10-14) · [17](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary The paper addresses other-anaphors: noun phrases modified by "other" or "another" whose antecedents must be found in the preceding discourse rather than in the same syntactic structure. The authors built a corpus of 500 such anaphors from the Wall Street Journal portion of the Penn Treebank (release 2), with manually annotated NP antecedents. They then trained a Naive Bayes classifier from Weka on anaphor–candidate pairs. The first feature set (F1) has nine features covering surface form, grammatical role, sentence distance and string matching. It also includes semantic information drawn from WordNet and the ANNIE named-entity tagger in GATE2. Error analysis showed that this lexical knowledge often falls short, because of rare word senses, missing relations and context-dependent links. To close this gap, the authors queried Google with lexico-syntactic patterns specific to other-anaphora. They turned the page counts into a mutual-information score and marked the best-scoring candidate for each anaphor. Adding this cheap Web feature clearly improved results. The work is the first machine-learning approach to anaphora resolution that draws on the Web, and it can be adapted to other anaphoric phenomena. ## Key points - Data: 500 other-anaphors from the Wall Street Journal (Penn Treebank), giving 3,084 anaphor–antecedent pairs, of which 16% are positive. Evaluation used 10-fold cross-validation. - A string-matching and recency baseline reached 27.8% P/R/F. Naive Bayes with F1 reached P 51.7, R 40.6 and F 45.5. - With the Web feature (F1+Web), scores rose to P 60.8, R 53.4 and F 56.9. This is a gain of 11.4 F-points, about 25% relative. - The Web feature labels the top-scoring candidate "webfirst" and all other candidates "webrest", which introduces competition among antecedents. - Remaining errors come from patterns too rare even on the Web and from mistakes in named-entity classification (39.6% of antecedents are named entities). Pronoun candidates are not yet handled. *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: [Named entity recognition](https://md.research.elamur.ai/free/topics/named-entity-recognition/) · [Coreference resolution](https://md.research.elamur.ai/free/topics/coreference-resolution/) ## Cited by Cited by 17 papers of our library cite it (of the 140673 whose bibliographies we read); 10 of them have a page here, 7 not yet. - [Cascading Collective Classification for Bridging Anaphora Recognition using a Rich Linguistic Feature Set](https://md.research.elamur.ai/free/papers/hou-2013-cascading-collective-classification-bridging-anaphora/) - [Global Inference for Bridging Anaphora Resolution](https://md.research.elamur.ai/free/papers/hou-2013-global-inference-bridging-anaphora-resolution/) - [A Rule-Based System for Unrestricted Bridging Resolution: Recognizing Bridging Anaphora and Finding Links to Antecedents](https://md.research.elamur.ai/free/papers/hou-2014-rule-based-system-unrestricted-bridging/) - [Bridging Resolution: A Survey of the State of the Art](https://md.research.elamur.ai/free/papers/kobayashi-2020-bridging-resolution-survey-state-art/) - [Comparing Knowledge Sources for Nominal Anaphora Resolution](https://md.research.elamur.ai/free/papers/markert-2005-comparing-knowledge-sources-nominal-anaphora/) - [Collective Classification for Fine-grained Information Status](https://md.research.elamur.ai/free/papers/markert-2012-collective-classification-fine-grained-information/) - [Supervised Noun Phrase Coreference Research: The First Fifteen Years](https://md.research.elamur.ai/free/papers/ng-2010-supervised-noun-phrase-coreference-research/) - [Resolving Complex Cases of Definite Pronouns: The Winograd Schema Challenge](https://md.research.elamur.ai/free/papers/rahman-2012-resolving-complex-cases-definite-pronouns/) - [Improving Pronoun Resolution Using Statistics-Based Semantic Compatibility Information](https://md.research.elamur.ai/free/papers/yang-2005-improving-pronoun-resolution-statistics-based/) - [Coreference Resolution Using Semantic Relatedness Information from Automatically Discovered Patterns](https://md.research.elamur.ai/free/papers/yang-2007-coreference-resolution-semantic-relatedness-information/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{modjeska2003using, title = {{Using the Web in Machine Learning for Other-Anaphora Resolution}}, author = {Modjeska, Natalia N. and Markert, Katja and Nissim, Malvina}, booktitle = {Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing}, year = {2003}, doi = {10.3115/1119355.1119378}, url = {https://doi.org/10.3115/1119355.1119378} } ``` - [← previous Undisclosed Speech: Patterns of Communication in the Book of Isaiah](https://md.research.elamur.ai/free/papers/hbo-2003-liss-undisclosed-speech-patterns-communication-book/) - [next → Which bridges for bridging definite descriptions?](https://md.research.elamur.ai/free/papers/gardent-2003-which-bridges-bridging-definite-descriptions/) - [↑ back to papers of 2003](https://md.research.elamur.ai/free/papers/2003/) ## References 19 references, 4 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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