--- title: "Bootstrapping Path-Based Pronoun Resolution — Bergsma 2006" description: "The paper targets a gap in third-person pronoun resolution." authors: ["Shane Bergsma", "Dekang Lin"] year: 2006 venue: "Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics" source: "https://aclanthology.org/P06-1005/" doi: "10.3115/1220175.1220180" 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/bergsma-2006-bootstrapping-path-based-pronoun-resolution/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/bergsma-2006-bootstrapping-path-based-pronoun-resolution/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2006](https://md.research.elamur.ai/free/en/papers/2006/) / Bootstrapping Path-Based Pronoun Resolution # Bootstrapping Path-Based Pronoun Resolution **Authors:** [Shane Bergsma](https://md.research.elamur.ai/free/en/authors/shane-bergsma/), [Dekang Lin](https://md.research.elamur.ai/free/en/authors/dekang-lin/) · **Year:** 2006 · **Venue:** Proceedings of the 21st International Conference on Computational Linguistics and 44th 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/P06-1005/) · [PDF](https://aclanthology.org/P06-1005.pdf) · [DOI 10.3115/1220175.1220180](https://doi.org/10.3115/1220175.1220180) · [OpenAlex](https://openalex.org/W2099115159) **Citations:** 129 citations (openalex-snapshot · 2026-10-14) · [64](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary The paper targets a gap in third-person pronoun resolution. Standard systems rely on gender/number agreement and syntactic constraints, so they cannot distinguish pairs like "John needs his friend" (coreferent) from "John needs his support" (not coreferent). Bergsma and Lin propose path coreference, which scores the dependency path between a candidate noun and a pronoun. Each path's coreference likelihood is learned by bootstrapping from a large news corpus parsed with Minipar. The method counts how often a path's endpoints agree or clash in gender/number and turns these counts into a probability. Highly coreferent paths are then reused to mine probabilistic gender/number statistics for nouns, which in turn refine the path counts. These statistical features are combined with conventional cues in an SVM classifier that ranks antecedent candidates. Evaluated on the ANC, AQT and MUC datasets, the new features give statistically significant improvements on the two larger sets. The approach captures world knowledge and corrects some parser errors that Binding Theory alone cannot handle. ## Key points - **Path coreference score.** For each path p, C(p) = N_S(p) / (N_S(p) + N_D(p)), where N_S and N_D count same-group and different-group terminals. Pronouns are split into seven gender/number/person groups. - **Path normalisation and two-stage extraction.** - Paths use lemmatised verbs and nouns, and named entities are replaced by their Minipar category. - Off-path modifiers are dropped, and paths are capped at eight nodes. - Extraction first uses pronoun–pronoun paths, then re-bootstraps with noun–pronoun paths. - **Gender/number lexicon.** Bootstrapping from coreferent paths yields what the authors call the most complete and accurate probabilistic gender table available at the time. Gender turned out to be the strongest single probabilistic feature. - **Results.** On ANC and AQT, each probabilistic feature produced a significant gain under McNemar's test (p=0.05). On the smaller MUC set the differences were not significant, although the relative contributions of the features were consistent. - **Extensions.** Preliminary experiments suggest path statistics can also help detect pleonastic pronouns and cataphora. Expanding paths with distributionally similar words did not help. *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/en/shelves/linguistics/) Topics: [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) ## Cited by Cited by 64 papers of our library cite it (of the 140673 whose bibliographies we read); 30 of them have a page here, 34 not yet. - [Coreference Semantics from Web Features](https://md.research.elamur.ai/free/en/papers/bansal-2012-coreference-semantics-web-features/) - [Learning Structured Perceptrons for Coreference Resolution with Latent Antecedents and Non-local Features](https://md.research.elamur.ai/free/en/papers/bjorkelund-2014-learning-structured-perceptrons-coreference-resolution/) - [Toward Gender-Inclusive Coreference Resolution](https://md.research.elamur.ai/free/en/papers/cao-2020-gender-inclusive-coreference-resolution/) - [Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle*](https://md.research.elamur.ai/free/en/papers/cao-2021-gender-inclusive-coreference-resolution-analysis/) - [Chinese Zero Pronoun Resolution: An Unsupervised Probabilistic Model Rivaling Supervised Resolvers](https://md.research.elamur.ai/free/en/papers/chen-2014-chinese-zero-pronoun-resolution-unsupervised/) - [Robust Coreference Resolution and Entity Linking on Dialogues: Character Identification on TV Show Transcripts](https://md.research.elamur.ai/free/en/papers/chen-2017-robust-coreference-resolution-entity-linking/) - [Do UD Trees Match Mention Spans in Coreference Annotations?](https://md.research.elamur.ai/free/en/papers/coref-2021-popel-do-ud-trees-match-mention/) - [Decentralized Entity-Level Modeling for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/durrett-2013-decentralized-entity-level-modeling-coreference/) - [Easy Victories and Uphill Battles in Coreference Resolution](https://md.research.elamur.ai/free/en/papers/durrett-2013-easy-victories-uphill-battles-coreference/) - [The KnowRef Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution](https://md.research.elamur.ai/free/en/papers/emami-2019-knowref-coreference-corpus-removing-gender/) - [Latent Trees for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/fernandes-2014-latent-trees-coreference-resolution/) - [Deterministic Coreference Resolution Based on Entity-Centric, Precision-Ranked Rules](https://md.research.elamur.ai/free/en/papers/lee-2013-deterministic-coreference-resolution-based-entity/) - [Unsupervised Ranking Model for Entity Coreference Resolution](https://md.research.elamur.ai/free/en/papers/ma-2016-unsupervised-ranking-model-entity-coreference/) - [Transferring Coreference Resolvers with Posterior Regularization](https://md.research.elamur.ai/free/en/papers/martins-2015-transferring-coreference-resolvers-posterior-regularization/) - [Multigraph Clustering for Unsupervised Coreference Resolution](https://md.research.elamur.ai/free/en/papers/martschat-2013-multigraph-clustering-unsupervised-coreference-resolution/) - [Latent Structures for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/martschat-2015-latent-structures-coreference-resolution/) - [Supervised Noun Phrase Coreference Research: The First Fifteen Years](https://md.research.elamur.ai/free/en/papers/ng-2010-supervised-noun-phrase-coreference-research/) - [CoNLL-2011 Shared Task: Modeling Unrestricted Coreference in OntoNotes](https://md.research.elamur.ai/free/en/papers/pradhan-2011-conll-2011-shared-task-modeling/) - [CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes](https://md.research.elamur.ai/free/en/papers/pradhan-2012-conll-2012-shared-task-modeling/) - [A Multi-Pass Sieve for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/raghunathan-2010-multi-pass-sieve-coreference-resolution/) - [Resolving Complex Cases of Definite Pronouns: The Winograd Schema Challenge](https://md.research.elamur.ai/free/en/papers/rahman-2012-resolving-complex-cases-definite-pronouns/) - [IMS HotCoref DE: A Data-driven Co-reference Resolver for German](https://md.research.elamur.ai/free/en/papers/roesiger-2016-ims-hotcoref-de-data-driven/) - [Gender Bias in Coreference Resolution](https://md.research.elamur.ai/free/en/papers/rudinger-2018-gender-bias-coreference-resolution/) - [RelaxCor Participation in CoNLL Shared Task on Coreference Resolution](https://md.research.elamur.ai/free/en/papers/sapena-2011-relaxcor-participation-conll-shared-task/) - [Easy-first Coreference Resolution](https://md.research.elamur.ai/free/en/papers/stoyanov-2012-easy-first-coreference-resolution/) - [The More Antecedents, the Merrier: Resolving Multi-Antecedent Anaphors](https://md.research.elamur.ai/free/en/papers/vala-2016-more-antecedents-merrier-resolving-multi/) - [Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns](https://md.research.elamur.ai/free/en/papers/webster-2018-mind-gap-balanced-corpus-gendered/) - [A Mixed Deterministic Model for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/yuan-2012-mixed-deterministic-model-coreference-resolution/) - [Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods](https://md.research.elamur.ai/free/en/papers/zhao-2018-gender-bias-coreference-resolution-evaluation/) - [Context-Sensitive Convolution Tree Kernel for Pronoun Resolution](https://md.research.elamur.ai/free/en/papers/zhou-2008-context-sensitive-convolution-tree-kernel/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{bergsma2006bootstrapping, title = {{Bootstrapping Path-Based Pronoun Resolution}}, author = {Bergsma, Shane and Lin, Dekang}, booktitle = {Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics}, year = {2006}, doi = {10.3115/1220175.1220180}, url = {https://doi.org/10.3115/1220175.1220180} } ``` - [← previous BESTCUT: A Graph Algorithm for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/nicolae-2006-bestcut-graph-algorithm-coreference-resolution/) - [next → Broad-Coverage Sense Disambiguation and Information Extraction with a Supersense Sequence Tagger](https://md.research.elamur.ai/free/en/papers/ciaramita-2006-broad-coverage-sense-disambiguation-information/) - [↑ back to papers of 2006](https://md.research.elamur.ai/free/en/papers/2006/) ## References 20 references, 12 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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