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English Edit

System

Syntax feature?

[note 1]

Semantic role feature?

Semantic type feature?

[note 2]

Word-window feature? Mention-pair? Entity-mention? Mention-ranking? Cluster-ranking? Cluster-pair? Rule-based? Base ML model Integer linear programming? Reference Notes
cort Yes

[note 3]

No Yes

[note 4]

Yes Yes Yes No No No perceptron No Martschat and Strube (2015)[1]
nn_coref Yes

[note 5]

No Yes

[note 6]

No Yes No No No No neural net

(RNN for encoding clusters)

No Wiseman et al. (2016)[2]
huggingface's neural coref Yes No Yes

[note 7]

Yes

[note 8]

No No Yes No No No neural net No medium post impl. of Clark and Manning (2016)[3]
deep-coref(CoreNLP) Yes No Yes

[note 7]

Yes

[note 8]

Yes No Yes Yes No No neural net No Clark and Manning (2016)[4]
hcoref (Hybrid Coref) Yes

[note 9]

No Yes

[note 10]

No No Yes? No No Yes? Yes

[note 11]

random forest

[note 11]

No Lee et al. (2017a)[5]
dcoref(Stanford sieve) Yes No Yes

[note 12]

No No No No No Yes Yes None No Lee et al. (2013)[6] part of Stanford CoreNLP
Berkeley CR No No Yes No No Yes

[note 13]

Yes

[note 14]

No No No log-linear No Durrett and Klein (2013)[7]
Illinois CR No Yes
xrenner Yes None No Zeldes and Zhang (2016)[8]
e2e-coref No No No No No No Yes No No No feed-forward NN + LSTM + attention No Lee et al. (2017b)[9]
allennlp No No No No No No Yes No No No feed-forward NN + LSTM + attention No a reimplementation of e2e-coref with some changes

German Edit

System

Syntax feature?

[note 15]

Semantic role feature?

Semantic type feature?

[note 16]

Word-window feature? Mention-pair? Entity-mention? Mention-ranking? Cluster-ranking? Cluster-pair? Rule-based? Base ML model Integer linear programming? Reference Notes
CorZu University of Zurich
HotCoref University of Stuttgart

Notes Edit

  1. Meaning the syntactic relation between mentions or between mention and surrounding words. Head word features (which might come from a parser) is not considered a syntactic feature.
  2. Different from semantic role features, this includes features about mentions alone: semantic type (person/object/number), NER type (person/location/organization), or other taxonomies.
  3. deprel: dependency relation of a mention to its governor
  4. sem_class: one of 'PERSON', 'OBJECT', 'NUMERIC' and 'UNKNOWN' and head_ner: named entity tag of the mention's head word
  5. From syntactic ancestry features in BASIC+ (Wiseman et al. 2015)
  6. From entity type features in BASIC+ (Wiseman et al. 2015)
  7. 7.0 7.1 In Clark and Manning (2016): "The type of the mention (pronoun, nominal, proper, or list)"
  8. 8.0 8.1 From Clark and Manning (2016): "first word, last word, two preceding words, and two following words of the mention. Averaged word embed- dings of the five preceding words, five following words, all words in the mention, all words in the mention’s sentence, and all words in the mention’s document."
  9. Feature: "The path in the parse tree from the root to the (antecedent/anaphor)"
  10. Feature: "named entity type attributes of (antecedent/anaphor)"
  11. 11.0 11.1 They combine rule-based and statistical classifiers.
  12. "NER label – fromthe Stanford NER"
  13. TRANSITIVE model: "each mention to maintain its own distributions over values for a number of proper- ties; these properties could include gender, named- entity type, or semantic class. Then, we will require each anaphoric mention to agree with its antecedent on the value of each of these properties"
  14. BASIC model: "This approach is similar to the mention- ranking model of Rahman and Ng (2009)."
  15. Meaning the syntactic relation between mentions or between mention and surrounding words. Head word features (which might come from a parser) is not considered a syntactic feature.
  16. Different from semantic role features, this includes features about mentions alone: semantic type (person/object/number), NER type (person/location/organization), or other taxonomies.

References Edit

  1. Sebastian Martschat and Michael Strube. 2015. Latent structures for coreference resolution. TACL, 3:405– 418.
  2. Wiseman, Sam, Alexander M. Rush, and Stuart M. Shieber. "Learning Global Features for Coreference Resolution." arXiv preprint arXiv:1604.03035(2016).
  3. Clark, K., & Manning, C. D. (2016). Deep Reinforcement Learning for Mention-Ranking Coreference Models. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP-16), 2256–2262.
  4. Clark, K., & Manning, C. D. (2016a). Improving Coreference Resolution by Learning Entity-Level Distributed Representations. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 643–653. http://doi.org/10.18653/v1/P16-1061
  5. LEE, HEEYOUNG, MIHAI SURDEANU, and DAN JURAFSKY. "A scaffolding approach to coreference resolution integrating statistical and rule-based models." Natural Language Engineering (2017a): 1-30.
  6. Heeyoung Lee, Angel Chang, Yves Peirsman, Nathanael Chambers, Mihai Surdeanu and Dan Jurafsky. Deterministic coreference resolution based on entity-centric, precision-ranked rules. Computational Linguistics 39(4), 2013.
  7. Durrett, G., & Klein, D. (2013). Easy victories and uphill battles in coreference resolution. EMNLP ’13, (October), 1971–1982.
  8. Zeldes, A., & Zhang, S. (2016). When Annotation Schemes Change Rules Help: A Configurable Approach to Coreference beyond OntoNotes. Workshop on Coreference Resolution Beyond OntoNotes at NAACL, (Corbon), 92–101.
  9. Lee, K., He, L., Lewis, M., & Zettlemoyer, L. S. (2017b). End-to-end Neural Coreference Resolution. In EMNLP.