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Cascading Collective Classification for Bridging Anaphora Recognition using a Rich Linguistic Feature Set

Summary

The paper addresses the automatic recognition of bridging anaphora. These are noun phrases that are not coreferent with an earlier mention but are tied to it through an associative relation, as when "the lobby" follows "one building". Bridging is varied, has few surface cues and is rare, so earlier information status (IS) classifiers detected it poorly. Within the fine-grained IS framework of Markert et al. (2012), the authors add three groups of linguistically motivated features. The first captures discourse structure, such as sentences lacking entity coherence with prior context. The second is lexico-semantic, drawing on relational nouns, role nouns, building parts and set elements. The third helps separate generic NPs from bridging. They combine these features with a cascading minority preference algorithm: binary LibSVM classifiers for rare classes run from rarest to most frequent, and a multiclass model handles the remaining cases. Experiments on OntoNotes WSJ texts show much better bridging recognition while other IS classes keep their performance. The features and the cascade turn out to be complementary, which matters for coherence modelling and for applications such as prosody.

Key points

  • Data: 50 WSJ documents from OntoNotes (the ISNotes corpus) with about 11,000 NPs annotated for IS, including 663 bridging anaphors. Evaluation uses 10-fold cross-validation on documents, with gold mentions.
  • Fewer than 40% of bridging anaphors in the corpus are definite NPs, so restricting recognition to definites is not justified.
  • Lexical resources:
    • about 4,000 relational nouns taken from WordNet;
    • about 500 professional-role nouns from the General Inquirer;
    • building-part and spatial/temporal modifier lists from Inquirer;
    • frequent proper names from the Tipster corpus.
  • Adding the new features significantly raised overall accuracy over both baselines (McNemar's test, 1% level).
  • The cascade on its own, with only the baseline features, overgenerated rare classes and lowered accuracy. An ablation of the best system found that lexical and semantic features matter most, while discourse and genericity features contribute less.

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); 10 of them have a page here, 7 not yet.

Cite

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

@inproceedings{hou2013cascading,
  title = {{Cascading Collective Classification for Bridging Anaphora Recognition using a Rich Linguistic Feature Set}},
  author = {Hou, Yufang and Markert, Katja and Strube, Michael},
  booktitle = {Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing},
  year = {2013},
  doi = {10.18653/v1/d13-1077},
  url = {https://doi.org/10.18653/v1/d13-1077}
}

References

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