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Global Learning of Noun Phrase Anaphoricity in Coreference Resolution via Label Propagation

Summary

The paper investigates whether deciding up front if a noun phrase is anaphoric can reliably help coreference resolution. Earlier attempts to add this filtering step produced mixed results. The authors treat anaphoricity as a global, graph-based semi-supervised problem. They apply label propagation (Zhu and Ghahramani 2002), in which labeled NPs from training texts and unlabeled NPs from a test document form the vertices of one graph. Labels spread along weighted edges until the assignments stabilize, so every NP in a document is labeled jointly rather than one at a time. Edge weights come from one of two similarity functions: a feature-based RBF kernel or a convolution tree kernel over parse structures.

On the ACE 2003 corpus (NWIRE, NPAPER and BNEWS), the graph-based approach beats an SVM that classifies each NP independently. When used as a preprocessing filter for an SVM coreference system, it raises overall F1. The tree kernel helps with both pronouns and definite NPs, whereas the RBF kernel mainly helps pronouns. This suggests that syntactic structure matters most for spotting anaphoric definite descriptions.

Key points

  • Data: All experiments use ACE 2003 (NWIRE, NPAPER, BNEWS domains). The baseline coreference system follows the single-candidate feature set of Yang et al. (2003), using an SVM with an RBF kernel.
  • Anaphoricity accuracy, RBF kernel: Label propagation outperforms per-instance SVM classification. On NWIRE, accuracy on anaphoric/non-anaphoric NPs is 74.8/84.4% versus 71.3/80.2% for the SVM.
  • Anaphoricity accuracy, tree kernel: The best parse-tree scheme (DET) reaches 79.2/87.8% on NWIRE, compared with 76.5/82.3% for an SVM using the same structure. Removing antecedent-candidate, predicate or first-right-sibling information each costs roughly 3–5 points.
  • Coreference F1 gains: Overall F1 goes from 59.4/62.1/55.5 (baseline) to 60.7/63.8/57.4 with the RBF kernel and 61.8/65.2/59.6 with the tree kernel. Most of the improvement comes from higher precision.
  • Definite NPs: With the RBF kernel, definite-NP F1 barely changes. With the tree kernel, it rises by about 1.3–1.9 points (e.g., 40.2 → 42.1 on NWIRE).

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

10 papers of our library cite it (of the 140673 whose bibliographies we read); 3 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{zhou2009global,
  title = {{Global Learning of Noun Phrase Anaphoricity in Coreference Resolution via Label Propagation}},
  author = {Zhou, GuoDong and Kong, Fang},
  booktitle = {Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing},
  year = {2009},
  doi = {10.3115/1699571.1699640},
  url = {https://doi.org/10.3115/1699571.1699640}
}

References

28 references, 14 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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