---
title: "Enhanced Word Representations for Bridging — Hou 2018"
description: "Bridging anaphora resolution links an anaphor to an earlier antecedent through a non-identity relation, such as part-of, employment, or an encyclopedic…"
authors: ["Yufang Hou"]
year: 2018
venue: "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)"
source: "https://aclanthology.org/N18-2001/"
doi: "10.18653/v1/n18-2001"
arxiv: "1803.04790"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/hou-2018-enhanced-word-representations-bridging-anaphora/"
---
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# Enhanced Word Representations for Bridging Anaphora Resolution

**Authors:** [Yufang Hou](https://md.research.elamur.ai/free/authors/yufang-hou/) · **Year:** 2018 · **Venue:** Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/N18-2001/) · [PDF](https://aclanthology.org/N18-2001.pdf) · [DOI 10.18653/v1/n18-2001](https://doi.org/10.18653/v1/n18-2001) · [arXiv 1803.04790](https://arxiv.org/abs/1803.04790) · [OpenAlex](https://openalex.org/W2963189761)

**Citations:** 18 citations (openalex-snapshot · 2026-10-14) · [14](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Bridging anaphora resolution links an anaphor to an earlier antecedent through a non-identity relation, such as part-of, employment, or an encyclopedic association. Standard embeddings like GloVe mostly encode similarity between synonyms or words of the same type, while this task depends on relatedness between associated nouns. Hou builds a GloVe-based "PP context model" from Gigaword. Head–modifier noun pairs are taken from prepositional NPs (X of Y) and possessive NPs (Y's X), and each head is tagged with a "_PP" suffix so the vectors keep the asymmetry of the relation.

The resulting vectors, embeddings PP, are evaluated on the ISNotes corpus. A simple rule picks the candidate with the highest cosine similarity to the anaphor, and this alone reaches about 30% accuracy, well above standard GloVe vectors. Adding a single embedding-based constraint to the Markov logic system MLN model II (Hou et al., 2013b) raises its accuracy significantly.

The work matters because it produces a reusable, efficiently computable lexical resource for associative relations. Such a resource is hard to obtain from vanilla distributional embeddings.

## Key points
- **Training data and setup:** about 197 million noun pairs from automatically parsed Gigaword were treated as two-word "sentences."
  - Vocabulary: roughly 276k entries, including 188k distinct nouns without the suffix.
  - Settings: 100 dimensions, context window of 2, and words seen fewer than 10 times removed.
- **Evaluation data:** ISNotes covers 50 WSJ texts from OntoNotes, with about 11,000 NPs and 663 bridging anaphors.
  - Relation types: 14% part-of/attribute-of, 7% set, and 79% "other."
- **Candidate selection:** candidates come from the same sentence, the two previous sentences, and the text's first sentence, giving about 19 per anaphor on average.
- **Accuracy with embeddings alone:**
  - GloVe GigaWiki14: 18.10%
  - GloVe Giga: 19.00%
  - Same noun pairs without the "_PP" suffix: 22.17%
  - embeddings PP: 30.32%
  - The suffix is therefore the decisive factor.
- **Accuracy within MLN model II:** the baseline scores 41.32%, rising to 45.85% with embeddings PP (significant, p \< 0.01).
  - For comparison, adding GloVe constraints gives 39.52%, and adding the suffix-free vectors gives 40.42%.
  - In this setup, the NP vector averages the head with the common nouns that precede it.

*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

14 papers of our library cite it (of the 140673 whose bibliographies we read); 7 of them have a page here, 7 not yet.

- [ChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain](https://md.research.elamur.ai/free/papers/fang-2021-chemu-ref-corpus-modeling-anaphora/)
- [What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text](https://md.research.elamur.ai/free/papers/fang-2022-what-does-it-take-bake/)
- [A Deterministic Algorithm for Bridging Anaphora Resolution](https://md.research.elamur.ai/free/papers/hou-2018-deterministic-algorithm-bridging-anaphora-resolution/)
- [Bridging Anaphora Resolution as Question Answering](https://md.research.elamur.ai/free/papers/hou-2020-bridging-anaphora-resolution-as-question/)
- [Fine-grained Information Status Classification Using Discourse Context-Aware BERT](https://md.research.elamur.ai/free/papers/hou-2020-fine-grained-information-status-classification/)
- [Bridging Resolution: A Survey of the State of the Art](https://md.research.elamur.ai/free/papers/kobayashi-2020-bridging-resolution-survey-state-art/)
- [Multitask Learning-Based Neural Bridging Reference Resolution](https://md.research.elamur.ai/free/papers/yu-2020-multitask-learning-based-neural-bridging/)

## Cite

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

```bibtex
@inproceedings{hou2018enhanced,
  title = {{Enhanced Word Representations for Bridging Anaphora Resolution}},
  author = {Hou, Yufang},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
  year = {2018},
  doi = {10.18653/v1/n18-2001},
  eprint = {1803.04790},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/n18-2001}
}
```

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