A Multi-Pass Sieve for Coreference Resolution

Summary

The paper proposes an unsupervised, deterministic coreference system built as a sequence of "sieves": independent rule-based passes applied from the most precise to the least precise. Each pass sees the clusters built by earlier passes, so later, weaker rules can use attributes shared across a whole entity cluster rather than a single mention. Early passes handle high-confidence cases such as exact string match and appositive or predicate-nominative constructions; later passes relax head matching and finally resolve pronouns under agreement constraints. Candidate antecedents are ordered by a syntactic tree traversal in the spirit of Hobbs. The authors report that this simple, modular design was competitive with or better than learned systems of the time on standard benchmarks; it became the basis of the Stanford deterministic coreference system.

How Mova uses it

• coref/src/sieve.rs — the overall architecture: an ordered list of sieves from most to least precise (speaker, exact, relaxed, appos, pred, relpron, acronym, reflexive, strict head-match variants, proper_head, pronoun), each attaching only the first mention of its cluster and skipping indefinite mentions except on exact match.
• Candidate ordering in the Hobbs style, taken from the description in this paper and its follow-up (Lee et al. 2013): breadth-first left-to-right over the tree, own clause first, then the rest of the sentence, then previous sentences (left-to-right for pronouns, right-to-left for nominals).
• Cluster-level features: a cluster's number, gender, animacy and person are the union of its mentions' features, so pronoun agreement is checked against the whole entity.
• Every link carries the id of the sieve that made it, which coref explain prints as the reason.

Effectiveness in Mova

Measured on GUM test (UD 2.18), head match, CoNLL F1 with / without singletons, gold trees: the default sieve (12 passes) scores 73.04 / 60.31, versus exact string match only 53.07 / 27.88 and "pronoun → nearest agreeing mention" 57.73 / 32.67; on Mova's own predicted trees 67.90 / 54.03. On dev, Hobbs-style candidate ordering beats pure recency: pronoun precision 64.2% vs 55.4%, CoNLL 73.43 / 63.34 vs 72.23 / 61.33. Two relaxed sieves were disabled because their precision on dev was too low (strict_c 31%, relaxed_head 24%).

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Topics: Coreference resolution

Cite

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

@inproceedings{raghunathan2010multi,
  title = {{A Multi-Pass Sieve for Coreference Resolution}},
  author = {Raghunathan, Karthik and Lee, Heeyoung and Rangarajan, Sudarshan and Chambers, Nathanael and Surdeanu, Mihai and Jurafsky, Dan and Manning, Christopher},
  booktitle = {Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing},
  year = {2010},
  url = {https://aclanthology.org/D10-1048/}
}

References

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