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# Global Inference for Bridging Anaphora Resolution

**Authors:** [Yufang Hou](https://md.mova.elamur.ai/free/en/docs/papers/authors/yufang-hou/), [Katja Markert](https://md.mova.elamur.ai/free/en/docs/papers/authors/katja-markert/), [Michael Strube](https://md.mova.elamur.ai/free/en/docs/papers/authors/michael-strube/) · **Year:** 2013 · **Venue:** Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2013) **Link:** [aclanthology.org/N13-1111](https://aclanthology.org/N13-1111/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only

[**33** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/hou-2013-global-inference-bridging-anaphora-resolution/) of the 140673 papers whose bibliographies we read[Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies](https://aclanthology.org/volumes/N13-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/N13-1111/)
- [PDF](https://aclanthology.org/N13-1111.pdf)

**Authors (3)**

1. Yufang Hou
2. Katja Markert
3. Michael Strube

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

The paper addresses antecedent selection for bridging anaphora with no limits on anaphor or relation types. The usual pairwise model classifies each anaphor and candidate in isolation, with a fixed window. The authors use a Markov logic network instead. Local formulas use a rich feature set (preposition patterns, semantic relatedness, salience). Global formulas add constraints that anaphors in a text often share one antecedent, and that a salient entity is preferred. The corpus is 663 bridging NPs in OntoNotes (ISNotes), evaluated by 10-fold cross-validation on documents. The global model reaches 41.32% accuracy, against 36.35% for the best pairwise model. The gain is largest for antecedents three or more sentences away: 35.44% against 16.46%. The global model also reaches 91% of NP antecedents as candidates, against 77% for the local model.

## Ideas

- I1. Replace the window with a document-wide candidate pool, as the paper does, but keep it deterministic: a candidate is any NP already seen, ranked by a chain-length (salience) count that is computed from `coref` chains. Record the rank of the gold antecedent for each anaphor.
- I2. Add a one-antecedent-per-cluster rule as a tie-breaker: when several anaphors pick the same head, prefer the choice that keeps the earlier pick. Count how often this rule changes an answer.
- I3. Report accuracy by anaphor–antecedent sentence distance (0, 1, 2, three or more), as the paper's Table 5 does. This shows whether any new rule helps the long-distance cases.
- I4. The error analysis names two gaps: modifiers ("the local council") and non-NP antecedents (6% of cases). Measure each gap before adding code to `en/src/morph.rs` or the `coref` mention extractor.

## For Mova

The chapter is [35. Reference and coreference](https://md.mova.elamur.ai/free/en/docs/languages/en/35-reference-and-coreference/). The code is `coref/src/sieve.rs` and `coref/src/mention.rs`, with the candidate salience in the chain code. The measure is accuracy by distance bucket, run twice for determinism. The Markov logic network is not for Mova. The global constraints can be written as rules. ISNotes uses OntoNotes data, so treat it as evaluation only.

Topics: [Coreference resolution](https://md.mova.elamur.ai/free/en/docs/papers/topics/coreference-resolution/)

## Cite

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

```
@inproceedings{hou2013global,
  title = {{Global Inference for Bridging Anaphora Resolution}},
  author = {Hou, Yufang and Markert, Katja and Strube, Michael},
  booktitle = {Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2013},
  url = {https://aclanthology.org/N13-1111/}
}
```

- [← previous An Analysis of Natural Language Inference Benchmarks through the Lens of Negation](https://md.mova.elamur.ai/free/en/docs/papers/hossain-2020-analysis-natural-language-inference-benchmarks/)
- [next → Unrestricted Bridging Resolution](https://md.mova.elamur.ai/free/en/docs/papers/hou-2018-unrestricted-bridging-resolution/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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