A Joint Model for Entity Analysis: Coreference, Typing, and Linking

Summary

Durrett and Klein address three closely linked parts of entity analysis that are usually solved separately or in a fixed pipeline: coreference resolution, coarse semantic typing (NER), and entity linking to Wikipedia. Their argument is that each task can fix the others' mistakes, so no pipeline order is ideal. For example, a nearby "the company" signals that "Dell" means the firm rather than the person.

They build a single structured conditional random field with one antecedent, type, and link variable per mention, plus latent Wikipedia query variables. Unary factors carry features from strong standalone baselines. Binary and ternary factors softly reward consistency across tasks, such as coreferent mentions sharing a type, and map NER types to Wikipedia infoboxes, categories, and article text. Exact inference is intractable, so they use belief propagation after pruning variable domains with a coarse model.

On ACE 2005 and OntoNotes, the joint model beats its independent baselines on every task and reaches state-of-the-art results. This shows that joint modeling can stay modular and efficient.

Key points

Evaluation covers two corpora with different mention standards and annotation layers: ACE 2005 and OntoNotes 5.0 (CoNLL 2011/2012 splits).
On the OntoNotes / CoNLL 2011 test set, NER F1 reaches 84.04. This compares with 82.64 for the independent model (+1.40), 83.45 for the Illinois system, and 82.24 for Passos et al. (2014). The joint model has higher precision, while Illinois has higher recall.
On ACE, entity linking matches Fahrni and Strube (2014). On OntoNotes, coreference is on par with Björkelund and Kuhn (2014), the best published system.
Ablations show that removing any pairwise cross-task interaction hurts at least one task. The joint system runs only about twice as slow as the three separate models combined.
The code is released as the Berkeley Entity Resolution System (nlp.cs.berkeley.edu). The paper appeared in TACL, vol. 2, 2014.

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: Named entity recognition · Coreference resolution

Cited by

95 papers of our library cite it (of the 140673 whose bibliographies we read); 19 of them have a page here, 76 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl_a_00197

@article{durrett2014joint,
  title = {{A Joint Model for Entity Analysis: Coreference, Typing, and Linking}},
  author = {Durrett, Greg and Klein, Dan},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2014},
  doi = {10.1162/tacl\_a\_00197},
  url = {https://doi.org/10.1162/tacl_a_00197}
}

References

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📄 A Joint Model for Entity Analysis: Coreference, Typing, and Linking