Coreference Resolution in a Modular, Entity-Centered Model

Summary

The paper addresses coreference resolution, focusing on a common source of errors: weak modeling of whether two mentions are semantically compatible. Earlier systems either listed compatible headword pairs or used coarse NER labels. Haghighi and Klein instead propose a generative, mostly unsupervised model with three separate modules. A semantic module draws latent entity types, such as PERSON, ORG, WEAPON or VEHICLE. Each type generates specific entities, and each entity holds word lists for properties like heads, modifiers and governors. A discourse module chooses antecedents with a log-linear, feature-based distance-dependent Chinese Restaurant Process. A mention module then builds each mention from its entity and type. The only supervision is a short list of prototype words per type. Lexical statistics are learned from large amounts of unlabeled text and shared at the type level. On ACE data with automatically detected mentions, the model beat strong supervised and deterministic systems on every metric reported. This shows that rich unsupervised semantic knowledge can lift end-to-end coreference performance.

Key points

The model has three layers (types → entities → mentions). Each type stores a word distribution plus a "fertility" distribution over list lengths for each property, so knowledge is shared at both the entity level and the type level.
Seed types are the ACE categories (PERS, ORG, GPE, WEA, VEH, LOC, FAC) plus an added GROUP type. Each gets up to four prototype heads taken from A04CU and A05ST. An equal number of unconstrained "other" types is induced automatically.
Evaluation used MUC, B³ All, B³ None and Pairwise F1 on ACE sets including A04CU, A05ST and A05RA. The model outperformed Stoyanov et al. (2009), Rahman and Ng (2009) and Haghighi and Klein (2009). Scores on A05RA were lower because its blog and forum text hurts the parser.
In the oracle-filtered mention setting on A04CU, the model reaches 80.3 B³, against 78.4 reported by Bengston and Roth (2008).
Used on its own as an NER classifier, the type component reaches 83.2 accuracy on the A05ST dev set. Most of its errors confuse GPE with ORG.

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

76 papers of our library cite it (of the 140673 whose bibliographies we read); 40 of them have a page here, 36 not yet.

Cite

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

@inproceedings{haghighi2010coreference,
  title = {{Coreference Resolution in a Modular, Entity-Centered Model}},
  author = {Haghighi, Aria and Klein, Dan},
  booktitle = {Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics},
  year = {2010},
  url = {https://aclanthology.org/N10-1061/}
}

References

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