---
title: "Kernel-Based Pronoun Resolution with Structured — Yang 2006"
description: "Choosing the right antecedent for a pronoun depends heavily on syntax."
authors: ["Xiaofeng Yang", "Jian Su", "Chew Lim Tan"]
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/P06-1006/"
doi: "10.3115/1220175.1220181"
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/yang-2006-kernel-based-pronoun-resolution-structured/"
---
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# Kernel-Based Pronoun Resolution with Structured Syntactic Knowledge

**Authors:** [Xiaofeng Yang](https://md.research.elamur.ai/free/authors/xiaofeng-yang/), [Jian Su](https://md.research.elamur.ai/free/authors/jian-su/), [Chew Lim Tan](https://md.research.elamur.ai/free/authors/chew-lim-tan/) · **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/P06-1006/) · [PDF](https://aclanthology.org/P06-1006.pdf) · [DOI 10.3115/1220175.1220181](https://doi.org/10.3115/1220175.1220181) · [OpenAlex](https://openalex.org/W2121028468)

**Citations:** 75 citations (openalex-snapshot · 2026-10-14) · [25](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Choosing the right antecedent for a pronoun depends heavily on syntax. Earlier systems, however, reduced parse trees to hand-picked flat features such as grammatical role, and those features may miss useful structure. Yang, Su and Tan let the learner work with the parse tree itself. Each pronoun–candidate pair receives a tree fragment as a structured feature. A convolution tree kernel scores how similar two fragments are. A composite kernel then merges that score with conventional features inside an SVM classifier, following the Soon et al. / Ng and Cardie learning framework. Sentence parses are linked under one root, so pairs that span sentences can still share a tree. The authors compare three ways of cutting out the fragment, varying how much context they keep. On the ACE data, covering three domains, adding the tree feature raises success rates by roughly 5–8%. The gain holds across training sizes and parsers. The work shows that kernels can discover syntactic cues for anaphora without manual feature design.

## Key points
- Three tree-fragment variants are tested: Min-Expansion, Simple-Expansion and Full-Expansion. The Simple version (S Simple) is used in the main comparisons, both alone and combined with the baseline features (NORM+S Simple).
- Evaluation uses the ACE corpus in three domains: NWire, NPaper and BNews. With Charniak's parser, NORM+S Simple reaches success rates of 79.2, 82.7 and 82.3 respectively.
- Removing the Subject and Object features from the baseline lowers success by as much as 5.7% on NWire. With the tree feature present, the loss falls to 1.9% (NWire) and 1.1% (BNews), and NPaper improves slightly.
- The Luo and Zitouni (2005) flat syntactic features, which are based on binding theory and c-command, add at most 1.3%. This gain is smaller than the one from the structured feature.
- Swapping Charniak (2000) for Collins (1999, Model II) changes results by less than 2% in every domain, so the approach holds up across standard parsers.

*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

25 papers of our library cite it (of the 140673 whose bibliographies we read); 10 of them have a page here, 15 not yet.

- [EM Works for Pronoun Anaphora Resolution](https://md.research.elamur.ai/free/papers/charniak-2009-em-works-pronoun-anaphora-resolution/)
- [A Unified Event Coreference Resolution by Integrating Multiple Resolvers](https://md.research.elamur.ai/free/papers/chen-2011-unified-event-coreference-resolution-integrating/)
- [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/)
- [Supervised Noun Phrase Coreference Research: The First Fifteen Years](https://md.research.elamur.ai/free/papers/ng-2010-supervised-noun-phrase-coreference-research/)
- [A Constraint-Based Hypergraph Partitioning Approach to Coreference Resolution](https://md.research.elamur.ai/free/papers/sapena-2013-constraint-based-hypergraph-partitioning-approach/)
- [BART: A Modular Toolkit for Coreference Resolution](https://md.research.elamur.ai/free/papers/versley-2008-bart-modular-toolkit-coreference-resolution/)
- [Coreference Systems Based on Kernels Methods](https://md.research.elamur.ai/free/papers/versley-2008-coreference-systems-based-kernels-methods/)
- [Context-Sensitive Convolution Tree Kernel for Pronoun Resolution](https://md.research.elamur.ai/free/papers/zhou-2008-context-sensitive-convolution-tree-kernel/)
- [Global Learning of Noun Phrase Anaphoricity in Coreference Resolution via Label Propagation](https://md.research.elamur.ai/free/papers/zhou-2009-global-learning-noun-phrase-anaphoricity/)

## Cite

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

```bibtex
@inproceedings{yang2006kernel,
  title = {{Kernel-Based Pronoun Resolution with Structured Syntactic Knowledge}},
  author = {Yang, Xiaofeng and Su, Jian and Tan, Chew Lim},
  booktitle = {Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics},
  year = {2006},
  doi = {10.3115/1220175.1220181},
  url = {https://doi.org/10.3115/1220175.1220181}
}
```

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## References

18 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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