---
title: "Dependency-driven Anaphoricity Determination for — Kong 2010"
description: "Coreference systems waste effort and make errors when they search for antecedents of noun phrases that actually introduce new entities."
authors: ["Fang Kong", "Guodong Zhou", "Longhua Qian", "Qiaoming Zhu"]
year: 2010
venue: "Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)"
source: "https://aclanthology.org/C10-1068/"
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-2010-dependency-driven-anaphoricity-determination-coreference/"
---
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# Dependency-driven Anaphoricity Determination for Coreference Resolution

**Authors:** [Fang Kong](https://md.research.elamur.ai/free/authors/fang-kong/), [Guodong Zhou](https://md.research.elamur.ai/free/authors/guodong-zhou/), Longhua Qian, [Qiaoming Zhu](https://md.research.elamur.ai/free/authors/qiaoming-zhu/) · **Year:** 2010 · **Venue:** Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010) · **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/C10-1068/) · [PDF](https://aclanthology.org/C10-1068.pdf) · [OpenAlex](https://openalex.org/W2124934171)

**Citations:** 21 citations (openalex-snapshot · 2026-10-14) · 8 in our library, of the 140673 papers whose bibliographies we read

## Summary
Coreference systems waste effort and make errors when they search for antecedents of noun phrases that actually introduce new entities. Kong et al. address anaphoricity determination, which means deciding in advance whether a mention is anaphoric, with a tree kernel classifier over syntactic parses. Their main contribution is a way to prune the full parse tree around each mention. Guided by constituent dependencies seen from both syntactic and semantic angles, the method keeps the nodes and paths relevant to the mention and discards noisy material. The result is a dependency-driven dynamic syntactic parse tree (D-DSPT). On the ACE 2003 corpus, D-DSPT outperforms earlier tree structures on the anaphoricity task. When the resulting classifier is placed in front of a learning-based coreference resolver, F1 rises clearly in all three domains, giving the best reported results at the time. The study shows that carefully shaped structural input matters for kernel methods, and that an explicit anaphoricity filter can substantially help coreference resolution.

## Key points
- The D-DSPT module improves coreference F1 over a baseline without anaphoricity determination by 4.1, 3.9 and 5.0 points, reaching 63.2 (NWIRE), 67.4 (NPAPER) and 61.6 (BNEWS) on ACE 2003. The gains come mostly from higher precision with only a small loss in recall.
- Gold-standard anaphoricity would add 6.4, 6.1 and 7.0 F1 over the baseline. D-DSPT therefore still trails this upper bound by roughly 2 points per domain (2.3, 2.2, 2.0).
- Improvements appear for pronominal, definite and indefinite NPs. Proper NPs gain almost nothing because the baseline's abbreviation feature already handles them well.
- The system beats Zhou and Kong (2009), which used a dynamically-extended tree, by 1.4, 2.2 and 2.0 F1. It also outperforms Ng (2009), which used graph minimum cuts.
- The authors present this as the first work to exploit constituent dependencies directly in tree kernel-based anaphoricity determination. The approach builds on the convolution tree kernel and on the dynamic parse trees of Qian et al. (2008) for relation extraction.

*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/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{kong2010dependency,
  title = {{Dependency-driven Anaphoricity Determination for Coreference Resolution}},
  author = {Kong, Fang and Zhou, Guodong and Qian, Longhua and Zhu, Qiaoming},
  booktitle = {Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)},
  year = {2010},
  url = {https://aclanthology.org/C10-1068/}
}
```

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## References

31 references, 14 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. D. Bean and E. Riloff 1999. Corpus-based Identification of Non-Anaphoric Noun Phrases. [find](https://scholar.google.com/scholar?q=D%2E+Bean+and+E%2E+Riloff+1999%2E+Corpus%2Dbased+Identification+of+Non%2DAnaphoric+Noun+Phrases%2E) [our card](https://md.research.elamur.ai/free/papers/bean-1999-corpus-based-identification-non-anaphoric/)
2. S. Bergsma, D. Lin and R. Goebel 2008. Distributional Identification of Non-referential Pronouns. ACL’2008 [find](https://scholar.google.com/scholar?q=S%2E+Bergsma%2C+D%2E+Lin+and+R%2E+Goebel+2008%2E+Distributional+Identification+of+Non%2Dreferential+Pronouns%2E+ACL%E2%80%992008)
3. C. Cherry and S. Bergsma. 2005. An expectation maximization approach to pronoun resolution. CoNLL’2005 [find](https://scholar.google.com/scholar?q=C%2E+Cherry+and+S%2E+Bergsma%2E+2005%2E+An+expectation+maximization+approach+to+pronoun+resolution%2E+CoNLL%E2%80%992005) [our card](https://md.research.elamur.ai/free/papers/cherry-2005-expectation-maximization-approach-pronoun-resolution/)
4. M. Collins and N. Duffy. 2001. Covolution kernels for natural language. NIPS̓2001 [find](https://scholar.google.com/scholar?q=M%2E+Collins+and+N%2E+Duffy%2E+2001%2E+Covolution+kernels+for+natural+language%2E+NIPS%CD%832001)
5. M. Denber 1998. Automatic Resolution of Anapho-ria in English. Technical Report, Eastman Ko-dakCo. [find](https://scholar.google.com/scholar?q=M%2E+Denber+1998%2E+Automatic+Resolution+of+Anapho%2Dria+in+English%2E+Technical+Report%2C+Eastman+Ko%2DdakCo%2E)
6. P. Denis and J. Baldridge. 2007. Global, joint determination of anaphoricity and coreference resolution using integer programming. NAACL/HLT’2007 [find](https://scholar.google.com/scholar?q=P%2E+Denis+and+J%2E+Baldridge%2E+2007%2E+Global%2C+joint+determination+of+anaphoricity+and+coreference+resolution+using+integer+programming%2E+NAACL%2FHLT%E2%80%992007) [our card](https://md.research.elamur.ai/free/papers/denis-2007-joint-determination-anaphoricity-coreference-resolution/)
7. R. Evans 2001. Applying machine learning toward an automatic classification of it. Literary and Linguistic Computing, 16(1):45-57 [find](https://scholar.google.com/scholar?q=R%2E+Evans+2001%2E+Applying+machine+learning+toward+an+automatic+classification+of+it%2E+Literary+and+Linguistic+Computing%2C+16%281%29%3A45%2D57)
8. F. Kong, G.D. Zhou and Q.M. Zhu. 2009 Employing the Centering Theory in Pronoun Resolution from the Semantic Perspective. EMNLP’2009 [find](https://scholar.google.com/scholar?q=F%2E+Kong%2C+G%2ED%2E+Zhou+and+Q%2EM%2E+Zhu%2E+2009+Employing+the+Centering+Theory+in+Pronoun+Resolution+from+the+Semantic+Perspective%2E+EMNLP%E2%80%992009) [our card](https://md.research.elamur.ai/free/papers/kong-2009-employing-centering-theory-pronoun-resolution/)
9. F. Kong, Y.C. Li, G.D. Zhou and Q.M. Zhu. 2009. Exploring Syntactic Features for Pronoun Resolution Using Context-Sensitive Convolution Tree Kernel. IALP’2009 [find](https://scholar.google.com/scholar?q=F%2E+Kong%2C+Y%2EC%2E+Li%2C+G%2ED%2E+Zhou+and+Q%2EM%2E+Zhu%2E+2009%2E+Exploring+Syntactic+Features+for+Pronoun+Resolution+Using+Context%2DSensitive+Convolution+Tree+Kernel%2E+IALP%E2%80%992009)
10. S. Lappin and J. L. Herbert. 1994. An algorithm for pronominal anaphora resolution. Computational Linguistics, 20(4) [find](https://scholar.google.com/scholar?q=S%2E+Lappin+and+J%2E+L%2E+Herbert%2E+1994%2E+An+algorithm+for+pronominal+anaphora+resolution%2E+Computational+Linguistics%2C+20%284%29) [our card](https://md.research.elamur.ai/free/papers/lappin-1994-algorithm-pronominal-anaphora-resolution/)
11. J.H. Li. G.D. Zhou, H. Zhao, Q.M. Zhu and P.D. Qian. Improving nominal SRL in Chinese language with verbal SRL information and automatic predicate recognition. EMNLP '2009 [find](https://scholar.google.com/scholar?q=J%2EH%2E+Li%2E+G%2ED%2E+Zhou%2C+H%2E+Zhao%2C+Q%2EM%2E+Zhu+and+P%2ED%2E+Qian%2E+Improving+nominal+SRL+in+Chinese+language+with+verbal+SRL+information+and+automatic+predicate+recognition%2E+EMNLP+%272009)
12. X. Luo. 2007. Coreference or not: A twin model for coreference resolution. NAACL-HLT’2007 [find](https://scholar.google.com/scholar?q=X%2E+Luo%2E+2007%2E+Coreference+or+not%3A+A+twin+model+for+coreference+resolution%2E+NAACL%2DHLT%E2%80%992007) [our card](https://md.research.elamur.ai/free/papers/luo-2007-coreference-or-not-twin-model/)
13. V. Ng and C. Cardie 2002. Identify Anaphoric and Non-Anaphoric Noun Phrases to Improve Coreference Resolution. COLING’2002 [find](https://scholar.google.com/scholar?q=V%2E+Ng+and+C%2E+Cardie+2002%2E+Identify+Anaphoric+and+Non%2DAnaphoric+Noun+Phrases+to+Improve+Coreference+Resolution%2E+COLING%E2%80%992002)
14. V. Ng and C. Cardie 2002. Improving machine learning approaches to coreference resolution. ACL’2002 [find](https://scholar.google.com/scholar?q=V%2E+Ng+and+C%2E+Cardie+2002%2E+Improving+machine+learning+approaches+to+coreference+resolution%2E+ACL%E2%80%992002) [our card](https://md.research.elamur.ai/free/papers/ng-2002-improving-machine-learning-approaches-coreference/)
15. V. Ng 2004. Learning Noun Phrase Anaphoricity to Improve Coreference Resolution: Issues in Representation and Optimization. [find](https://scholar.google.com/scholar?q=V%2E+Ng+2004%2E+Learning+Noun+Phrase+Anaphoricity+to+Improve+Coreference+Resolution%3A+Issues+in+Representation+and+Optimization%2E)
16. V. Ng 2009. Graph-cut based anaphoricity determination for coreference resolution. NAACL’2009 [find](https://scholar.google.com/scholar?q=V%2E+Ng+2009%2E+Graph%2Dcut+based+anaphoricity+determination+for+coreference+resolution%2E+NAACL%E2%80%992009) [our card](https://md.research.elamur.ai/free/papers/ng-2009-graph-cut-based-anaphoricity-determination/)
17. L.H. Qian, G.D. Zhou, F. Kong, Q.M. Zhu and P.D. Qian. 2008. Exploiting constituent dependencies for tree kernel-based semantic relation extraction. COLING’2008 [find](https://scholar.google.com/scholar?q=L%2EH%2E+Qian%2C+G%2ED%2E+Zhou%2C+F%2E+Kong%2C+Q%2EM%2E+Zhu+and+P%2ED%2E+Qian%2E+2008%2E+Exploiting+constituent+dependencies+for+tree+kernel%2Dbased+semantic+relation+extraction%2E+COLING%E2%80%992008)
18. W. M. Soon, H. T. Ng and D. Lim 2001. A machine learning approach to coreference resolution of noun phrase. Computational Linguistics, 27(4):521-544. [find](https://scholar.google.com/scholar?q=W%2E+M%2E+Soon%2C+H%2E+T%2E+Ng+and+D%2E+Lim+2001%2E+A+machine+learning+approach+to+coreference+resolution+of+noun+phrase%2E+Computational+Linguistics%2C+27%284%29%3A521%2D544%2E)
19. V. Stoyanov, N. Gilbert, C. Cardie and E. Riloff. 2009. Conundrums in Noun Phrase Coreference Resolution: Making Sense of the State-of-the Art. ACL’2009 [find](https://scholar.google.com/scholar?q=V%2E+Stoyanov%2C+N%2E+Gilbert%2C+C%2E+Cardie+and+E%2E+Riloff%2E+2009%2E+Conundrums+in+Noun+Phrase+Coreference+Resolution%3A+Making+Sense+of+the+State%2Dof%2Dthe+Art%2E+ACL%E2%80%992009) [our card](https://md.research.elamur.ai/free/papers/stoyanov-2009-conundrums-noun-phrase-coreference-resolution/)
20. B. L. Webber. 1979. A Formal Approach to Discourse Anaphora. Garland Publishing, Inc. [find](https://scholar.google.com/scholar?q=B%2E+L%2E+Webber%2E+1979%2E+A+Formal+Approach+to+Discourse+Anaphora%2E+Garland+Publishing%2C+Inc%2E)
21. X.F. Yang, G.D. Zhou, J. Su and C.L. Chew. 2003. Coreference Resolution Using Competition Learning Approach. ACL’2003 [find](https://scholar.google.com/scholar?q=X%2EF%2E+Yang%2C+G%2ED%2E+Zhou%2C+J%2E+Su+and+C%2EL%2E+Chew%2E+2003%2E+Coreference+Resolution+Using+Competition+Learning+Approach%2E+ACL%E2%80%992003) [our card](https://md.research.elamur.ai/free/papers/yang-2003-coreference-resolution-competition-learning-approach/)
22. X.F. Yang, J. Su and C.L. Chew. 2005. A Twin Candidate Model of Coreference Resolution with Non-Anaphor Identification Capability. IJCNLP’2005 [find](https://scholar.google.com/scholar?q=X%2EF%2E+Yang%2C+J%2E+Su+and+C%2EL%2E+Chew%2E+2005%2E+A+Twin+Candidate+Model+of+Coreference+Resolution+with+Non%2DAnaphor+Identification+Capability%2E+IJCNLP%E2%80%992005) [our card](https://md.research.elamur.ai/free/papers/yang-2005-twin-candidate-model-coreference-resolution/)
23. X.F. Yang, J. Su and C.L. Chew. 2006. Kernel-based pronoun resolution with structured syntactic knowledge. COLING-ACL’2006 [find](https://scholar.google.com/scholar?q=X%2EF%2E+Yang%2C+J%2E+Su+and+C%2EL%2E+Chew%2E+2006%2E+Kernel%2Dbased+pronoun+resolution+with+structured+syntactic+knowledge%2E+COLING%2DACL%E2%80%992006) [our card](https://md.research.elamur.ai/free/papers/yang-2006-kernel-based-pronoun-resolution-structured/)
24. X.F. Yang, J. Su and C.L. Tan 2008. A Twin-Candidate Model for Learning-Based Anaphora Resolution. Computational Linguistics 34(3):327-356 [find](https://scholar.google.com/scholar?q=X%2EF%2E+Yang%2C+J%2E+Su+and+C%2EL%2E+Tan+2008%2E+A+Twin%2DCandidate+Model+for+Learning%2DBased+Anaphora+Resolution%2E+Computational+Linguistics+34%283%29%3A327%2D356) [our card](https://md.research.elamur.ai/free/papers/yang-2008-twin-candidate-model-learning-based/)
25. M. Zhang, J. Zhang, J. Su and G.D. Zhou. 2006. A composite kernel to extract relations between entities with both flat and structured features. COLING/ACL’2006 [find](https://scholar.google.com/scholar?q=M%2E+Zhang%2C+J%2E+Zhang%2C+J%2E+Su+and+G%2ED%2E+Zhou%2E+2006%2E+A+composite+kernel+to+extract+relations+between+entities+with+both+flat+and+structured+features%2E+COLING%2FACL%E2%80%992006)
26. S. Zhao and R. Grishman. 2005. Extracting relations with integered information using kernel methods. ACL’2005 [find](https://scholar.google.com/scholar?q=S%2E+Zhao+and+R%2E+Grishman%2E+2005%2E+Extracting+relations+with+integered+information+using+kernel+methods%2E+ACL%E2%80%992005)
27. D. Zelenko, A. Chinatsu and R. Anthony. 2003. Kernel methods for relation extraction. Machine Learning Researching 3(2003):1083-1106 [find](https://scholar.google.com/scholar?q=D%2E+Zelenko%2C+A%2E+Chinatsu+and+R%2E+Anthony%2E+2003%2E+Kernel+methods+for+relation+extraction%2E+Machine+Learning+Researching+3%282003%29%3A1083%2D1106)
28. G.D. Zhou, F. Kong and Q.M. Zhu. 2008. Context-sensitive convolution tree kernel for pronoun resolution. IJCNLP’2008 [find](https://scholar.google.com/scholar?q=G%2ED%2E+Zhou%2C+F%2E+Kong+and+Q%2EM%2E+Zhu%2E+2008%2E+Context%2Dsensitive+convolution+tree+kernel+for+pronoun+resolution%2E+IJCNLP%E2%80%992008) [our card](https://md.research.elamur.ai/free/papers/zhou-2008-context-sensitive-convolution-tree-kernel/)
29. G.D. Zhou and F. Kong. 2009. Global Learning of Noun Phrase Anaphoricity in Coreference Resolution via Label Propagetion. EMNLP’2009 [find](https://scholar.google.com/scholar?q=G%2ED%2E+Zhou+and+F%2E+Kong%2E+2009%2E+Global+Learning+of+Noun+Phrase+Anaphoricity+in+Coreference+Resolution+via+Label+Propagetion%2E+EMNLP%E2%80%992009)
30. G.D. Zhou and J. Su. 2002. Named Entity recognition using a HMM-based chunk tagger. ACL’2002 [find](https://scholar.google.com/scholar?q=G%2ED%2E+Zhou+and+J%2E+Su%2E+2002%2E+Named+Entity+recognition+using+a+HMM%2Dbased+chunk+tagger%2E+ACL%E2%80%992002)
31. G.D. Zhou, M. Zhang, D.H. Ji and Q.M. Zhu. 2007. Tree kernel-based relation extraction with context-sensitive structured parse tree information. EMNLP/CoNLL’2007 [find](https://scholar.google.com/scholar?q=G%2ED%2E+Zhou%2C+M%2E+Zhang%2C+D%2EH%2E+Ji+and+Q%2EM%2E+Zhu%2E+2007%2E+Tree+kernel%2Dbased+relation+extraction+with+context%2Dsensitive+structured+parse+tree+information%2E+EMNLP%2FCoNLL%E2%80%992007)
