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Coreference Resolution Using Semantic Relatedness Information from Automatically Discovered Patterns

Summary

Coreference systems need to judge whether two noun phrases are semantically compatible. Lexicons such as WordNet lack many proper names and relation types. Earlier corpus-based methods relied on hand-picked templates like Hearst-style "X such as Y", which cover mostly is-a links. Yang and Su learn such templates automatically instead. NP pairs taken from coreference training instances act as positive and negative seeds. For each pair, a large corpus (Wikipedia) is searched for co-occurrences separated by no more than three tokens, and the surrounding context becomes a surface pattern. Patterns are ranked by frequency, filtered frequency or PMI-based reliability. They are then used in one of two ways: as individual top-ranked features, or combined into a single relatedness score (SRel). Both options feed a C5 decision-tree learner in the Soon et al. / Ng and Cardie framework. On ACE data, SRel works best, especially for proper names. It raises recall while keeping precision stable, showing that automatically discovered patterns can supply semantic knowledge that lexicons lack.

Key points

  • Evaluated on three ACE domains: NWire, NPaper and BNews. The baseline uses standard features such as string match, name alias, apposition and agreement, and is trained with C5. Training is extended with negative instances built from non-anaphors that the classifier wrongly resolved.
  • Seed pairs keep only the head word for common nouns and the full string for proper names. Pairs with pronouns or identical heads are skipped. Matched contexts become templates with <#t1#>/<#t2#> slots.
  • Using top-ranked pattern features raised recall by up to 3.3% in NWire. Precision fell by up to 1.2%, and F-measure improved by 1.9% in NWire, with no gain in the other two domains. Applying these features to all NP types, rather than only proper names, lowered precision considerably.
  • The single SRel feature reached F-measures of 67.1% (NWire), 65.0% (NPaper) and 62.7% (BNews), beating the baseline in every domain. In NWire the gain was a statistically significant 2.1% (t-test, p ≤ 0.05).
  • In the learned decision trees, pattern features appear near the top, close to name alias and apposition features. Most of the useful patterns are copula constructions such as "<t1> ) is a <t2>". The authors suggest Web-scale text as future work to address sparse co-occurrences.

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

17 papers of our library cite it (of the 140673 whose bibliographies we read); 8 of them have a page here, 9 not yet.

Cite

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

@inproceedings{yang2007coreference,
  title = {{Coreference Resolution Using Semantic Relatedness Information from Automatically Discovered Patterns}},
  author = {Yang, Xiaofeng and Su, Jian},
  booktitle = {Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics},
  year = {2007},
  url = {https://aclanthology.org/P07-1067/}
}

References

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