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A Rule-Based System for Unrestricted Bridging Resolution: Recognizing Bridging Anaphora and Finding Links to Antecedents

Summary

Bridging resolution links an anaphor to an antecedent that it relates to without referring to the same entity, such as a shuttle and its astronauts. Earlier work usually handled only one subtask at a time, either spotting anaphors or choosing antecedents. It also often restricted anaphors to definite NPs or relations to meronymy. This paper tackles the full, unrestricted problem end to end. The authors design eight linguistically motivated rules:

  • relational nouns, such as building parts and roles,
  • set membership,
  • links inspired by implicit semantic role labeling.

Each rule has its own candidate window, tuned on ten development documents. The data is ISNotes, a reliably annotated subset of OntoNotes WSJ texts. The rule system clearly beats a reimplementation of Vieira and Poesio (2000). It also edges out a learner that uses the same knowledge resources. Adding the rules and richer features to the learner improves results only marginally. The authors explain this by the many different bridging relations, which leave little training data per type. The work is presented as the first system for unrestricted bridging in a realistic setting.

Key points

  • Data: ISNotes covers 50 WSJ texts from OntoNotes, with about 11,000 NPs and 663 bridging anaphors.
    • Only 38.5% of anaphors are definite and 44.9% have no determiner.
    • 79% of relations fall into the "other" category, versus 14% part-of/attribute-of and 7% set.
  • Distance: 29% of anaphors have antecedents three or more sentences back.
  • Rule system: ruleSystem reaches R 11.9, P 42.9, F 18.6. The Vieira & Poesio baseline gets F 4.8, a significant gap (p < 0.01, randomization test).
  • Learning-based variants:
    • mlSystem ruleFeats: F 18.0
    • mlSystem ruleFeats+atomFeats: F 18.7
    • mlSystem atomFeats (no rule features): F 13.5
  • Error pattern: The learners have higher recall but lower precision. They often find the right anaphor but choose the wrong antecedent.

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

Topics: Coreference resolution

Cited by

23 papers of our library cite it (of the 140673 whose bibliographies we read); 12 of them have a page here, 11 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.3115/v1/d14-1222

@inproceedings{hou2014rule,
  title = {{A Rule-Based System for Unrestricted Bridging Resolution: Recognizing Bridging Anaphora and Finding Links to Antecedents}},
  author = {Hou, Yufang and Markert, Katja and Strube, Michael},
  booktitle = {Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2014},
  doi = {10.3115/v1/d14-1222},
  url = {https://doi.org/10.3115/v1/d14-1222}
}

References

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