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TODO:  Semantic role labeling and coreference resolution

TODO:

☀From Rahman & Ng (2012)[1]: "We employ narrative chains to heuristically predict the antecedent for the target pronoun, and encode the prediction as a feature. [...] we extract all the narrative chains containing both elements in the pair from Chambers and Jurafsky’s output."

From Khashabi & Roth (2015)[2] propose "Predicate schemas" and use them to solve Winnograd-style and regular coreference resolution tasks.

Algorithm Surface-

form

Discourse Syntactic

Structure

One-way

Selectional Preference

Event

Chain

Top F1 Source

Code

Notes
Haghighi and Klein (2010)[1] generative + log-linear?? Y Y Y N N 67.0 on ACE2004-Stoyanov

68.1 on ACE2005-Stoyanov

71.6 on ACE2005-Rahman

N[note 1]
Haghighi and Klein (2009)[2] Y N
Reconcile[3][4] Mention-pair Y Y[note 2] Y utah.edu
Neural Association Models N Y N Evaluated on Winnograd but not standard CR datasets
Rahman and Ng (2011)[5] role pairs predicate pairs N Evaluated on ACE and OntoNotes
Yang et al. (2005)[6] simple co-occurence statistics N
CherryPicker[7][8] Cluster-ranking Y Y Y N N utdallas.edu
BART Y Y Y N N bart-coref.org
Berkeley CR Mention-synchronous Y Y Y N N berkeley.edu
cort[9] Mention-ranking, latent structure Y Y Y N N github
Illinois CR[10] Mention-pair Y Y N N N cogcomp.org
Stanford sieve Sieve
Peng & Roth (2016)[11] (based on Illinois CR) Y Y Y N N frame-chains, frame-and-argument-chains 71.79 on ACE, 63.46 on CoNLL12 See Peng and Roth (2016)

TODO:

Notes Edit

  1. "Software release" is mentioned in footnote 11 but links don't contain the code anymore (as of October 2017).
  2. SameParagraph, SameSentence, SentNum, ParNum

References Edit

  1. Haghighi, A., & Klein, D. (2010). Coreference resolution in a modular, entity-centered model. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL, (June), 385–393. http://doi.org/10.3115/1608810.1608821
  2. Haghighi, A., & Klein, D. (2009). Simple Coreference Resolution with Rich Syntactic and Semantic Features. EMNLP 2009, 1152–1161.
  3. Stoyanov, V., Cardie, C., Gilbert, N., Riloff, E., Buttler, D., & Hysom, D. (2010). Reconcile : A Coreference Resolution Research Platform.
  4. Stoyanov, V., Cardie, C., Gilbert, N., Riloff, E., Buttler, D., & Hysom, D. (2010). Coreference resolution with reconcile. In Proceedings of the ACL 2010 Conference Short Papers (pp. 156–161).
  5. Rahman, A., & Ng, V. (2011). Coreference Resolution with World Knowledge. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (pp. 814–824).
  6. Yang, X., Su, J., & Tan, C. L. (2005). Improving Pronoun Resolution Using Statistics-based Semantic Compatibility Information. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (pp. 165–172). Stroudsburg, PA, USA: Association for Computational Linguistics. http://doi.org/10.3115/1219840.1219861
  7. Altaf Rahman and Vincent Ng. Supervised Models for Coreference Resolution. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pp. 968-977, 2009.
  8. Ng, V., & Cardie, C. (2002). Improving Machine Learning Approaches to Coreference Resolution. ACL 2012, 104–111.
  9. Sebastian Martschat and Michael Strube. 2015. Latent structures for coreference resolution. Transactions of the Association for Computational Linguis- tics, 3:405–418.
  10. Bengtson, E., & Roth, D. (2008). Understanding the value of features for coreference resolution. Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP ’08, 51(October), 294. http://doi.org/10.3115/1613715.1613756
  11. Peng, H., & Roth, D. (2016). Two Discourse Driven Language Models for Semantics. ACL 2016, 290–300.