--- title: "Employing the Centering Theory in Pronoun — Kong 2009" description: "Centering theory explains how discourse focus shifts between utterances." authors: ["Fang Kong", "GuoDong Zhou", "Qiaoming Zhu"] year: 2009 venue: "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing" source: "https://aclanthology.org/D09-1103/" 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/kong-2009-employing-centering-theory-pronoun-resolution/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/kong-2009-employing-centering-theory-pronoun-resolution/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2009](https://md.research.elamur.ai/free/papers/2009/) / Employing the Centering Theory in Pronoun Resolution from the Semantic Perspective # Employing the Centering Theory in Pronoun Resolution from the Semantic Perspective **Authors:** [Fang Kong](https://md.research.elamur.ai/free/authors/fang-kong/), [GuoDong Zhou](https://md.research.elamur.ai/free/authors/guodong-zhou/), [Qiaoming Zhu](https://md.research.elamur.ai/free/authors/qiaoming-zhu/) · **Year:** 2009 · **Venue:** Proceedings of the 2009 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/D09-1103/) · [PDF](https://aclanthology.org/D09-1103.pdf) · [OpenAlex](https://openalex.org/W2034284413) **Citations:** 19 citations (openalex-snapshot · 2026-10-14) · [10](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Centering theory explains how discourse focus shifts between utterances. Earlier attempts to use it for pronoun resolution, such as S-list features in learning-based systems, gave little benefit or even lowered F-measure. Kong, Zhou and Zhu argue that the problem lies in applying centering at the grammatical level, using subject/object ranking. They reformulate it at the semantic level instead. Semantic roles such as agent (ARG0) and patient are assigned with respect to the different predicates in a sentence. From these roles the authors derive centering-motivated features for a pronoun resolver: - semantic role features that model whether the local focus continues or shifts; - a ranking feature that places each pronoun among the sentence's pronouns by semantic role and surface position; - fine-grained pronoun subcategory features that make the other two features more discriminative. On the ACE 2003 corpus (NWIRE, NPAPER and BNEWS), these features consistently improve F-measure over a strong baseline. The gains hold for pronouns with antecedents in the same sentence and in earlier sentences. The authors present this as the first successful semantic-level use of centering for pronoun resolution. ## Key points - Overall pronoun resolution F-measure rises from 62.3 to 65.9 on NWIRE, from 65.3 to 69.8 on NPAPER, and from 59.0 to 66.7 on BNEWS. - Adding the relative pronominal ranking feature on top of the semantic role features yields +1.1, +3.0 and +5.5 F on the three domains. It helps most where antecedents tend to lie further back in the text. - Third-person pronoun F-measure improves by 8.3 on NWIRE, 0.9 on NPAPER and 4.7 on BNEWS. Reflexive pronouns drop on NPAPER, but they are rare in the corpus. - Gains appear at sentence distances of 0, 1 and 2, ranging from +2.7 to +12.5 F. This shows the features help both within and across sentences. - Pronoun subcategory features used alone hurt performance. They become useful only when combined with the semantic role and ranking features. *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/) · [Semantic role labeling](https://md.research.elamur.ai/free/topics/semantic-role-labeling/) ## Cited by Cited by 10 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 4 not yet. - [Resolving Object and Attribute Coreference in Opinion Mining](https://md.research.elamur.ai/free/papers/ding-2010-resolving-object-attribute-coreference-opinion/) - [Incorporating Syntax and Semantics in Coreference Resolution with Heterogeneous Graph Attention Network](https://md.research.elamur.ai/free/papers/jiang-2021-incorporating-syntax-semantics-coreference-resolution/) - [Dependency-driven Anaphoricity Determination for Coreference Resolution](https://md.research.elamur.ai/free/papers/kong-2010-dependency-driven-anaphoricity-determination-coreference/) - [A Tree Kernel-Based Unified Framework for Chinese Zero Anaphora Resolution](https://md.research.elamur.ai/free/papers/kong-2010-tree-kernel-based-unified-framework/) - [Exploiting Zero Pronouns to Improve Chinese Coreference Resolution](https://md.research.elamur.ai/free/papers/kong-2013-exploiting-zero-pronouns-improve-chinese/) - [Supervised Noun Phrase Coreference Research: The First Fifteen Years](https://md.research.elamur.ai/free/papers/ng-2010-supervised-noun-phrase-coreference-research/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{kong2009employing, title = {{Employing the Centering Theory in Pronoun Resolution from the Semantic Perspective}}, author = {Kong, Fang and Zhou, GuoDong and Zhu, Qiaoming}, booktitle = {Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing}, year = {2009}, doi = {10.3115/1699571.1699641}, url = {https://doi.org/10.3115/1699571.1699641} } ``` - [← previous EM Works for Pronoun Anaphora Resolution](https://md.research.elamur.ai/free/papers/charniak-2009-em-works-pronoun-anaphora-resolution/) - [next → Estimating the Sentence-Level Quality of Machine Translation Systems](https://md.research.elamur.ai/free/papers/specia-2009-estimating-sentence-level-quality-machine/) - [↑ back to papers of 2009](https://md.research.elamur.ai/free/papers/2009/) ## References 39 references, 13 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. C. 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