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Abstract

Interpreting anaphoric references is a fundamental aspect of our language competence that has long attracted the attention of computational linguists. The appearance of ever-larger anaphorically annotated data sets covering more and more anaphoric phenomena in ever-greater detail has spurred the development of increasingly more sophisticated computational models; as a result, the most recent state-of-the-art neural models are able to achieve impressive performance by leveraging linguistic, lexical, discourse, and encyclopedic information. This article provides a thorough survey of anaphora resolution (coreference) throughout this development, reviewing the available data sets and covering both the preneural history of the field and—in more detail—current neural models, including research on less-studied aspects of anaphoric interpretation such as bridging reference resolution and discourse deixis interpretation.

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2023-01-17
2024-04-27
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Literature Cited

  1. Aloraini A, Poesio M. 2020. Cross-lingual zero pronoun resolution. Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020)90–98. Paris: Eur. Lang. Resour. Assoc.
    [Google Scholar]
  2. Alshawi H 1992. The Core Language Engine Cambridge, MA: MIT Press
  3. Aone C, Bennett SW. 1995. Automatic acquisition of anaphora resolution strategies Paper presented at the AAAI Spring Symposium on Empirical Methods in Discourse: Interpretation and Generation, Palo Alto, CA, Mar. 27–29
  4. Asher N, Lascarides A. 2003. The Logic of Conversation Cambridge, UK: Cambridge Univ. Press
  5. Bagga A, Baldwin B. 1998. Algorithms for scoring coreference chains. Proceedings of the LREC Workshop on Linguistic Coreference563–66. Paris: Eur. Lang. Resour. Assoc.
    [Google Scholar]
  6. Baldwin B 1997. CogNIAC: high precision pronoun coreference with limited knowledge and precision preferences. Proceedings of the ACL'97/EACL'97 Workshop on Operational Factors in Practical, Robust Anaphora Resolution for Unrestricted Texts R Mitkov, B Boguraev 38–45. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  7. Bamman D, Lewke O, Mansoor A. 2020. An annotated dataset of coreference in English literature. Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020)44–54. Paris: Eur. Lang. Resour. Assoc.
    [Google Scholar]
  8. Barbu C, Mitkov R. 2001. Evaluation tool for rule-based anaphora resolution methods. Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics34–41. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  9. Baumgartner WA Jr., Bada M, Pyysalo S, Ciosici MR, Hailu N et al. 2019. Craft shared tasks overview—integrated structure, semantics, and coreference. Proceedings of the 5th Workshop on BioNLP174–84. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  10. Bengio Y, Ducharme R, Vincent P, Jauvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
    [Google Scholar]
  11. Bengtson E, Roth D. 2008. Understanding the value of features for coreference resolution. Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing294–303. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  12. Bergsma S. 2016. Extracting anaphoric agreement properties from corpora. See Poesio et al. 2016b 345–68
  13. Björkelund A, Kuhn J. 2014. Learning structured perceptrons for coreference resolution with latent antecedents and non-local features. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)47–57. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  14. Bos J. 2004. Computational semantics in discourse: underspecification, resolution, and inference. J. Logic Lang. Inform. 13:2139–57
    [Google Scholar]
  15. Büring D. 2005. Binding Theory Cambridge, UK: Cambridge Univ. Press
  16. Byron DK. 2002. Resolving pronominal references to abstract entities. Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics80–87. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  17. Carter DM. 1987. Interpreting Anaphors in Natural Language Texts Chichester, UK: Ellis Horwood
  18. Chen C, Ng V. 2016. Chinese zero pronoun resolution with deep neural networks. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)778–88. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  19. Chinchor NA, Sundheim B. 1995. Message Understanding Conference (MUC) tests of discourse processing Paper presented at the AAAI Spring Symposium on Empirical Methods in Discourse: Interpretation and Generation, Palo Alto, CA, Mar. 27–29
  20. Clark HH 1977. Bridging. Thinking: Readings in Cognitive Science PN Johnson-Laird, P Wason 411–20. London/New York: Cambridge Univ. Press
    [Google Scholar]
  21. Cohen KB, Lanfranchi A, Choi MJ-y, Bada M, Baumgartner WA Jr. et al. 2017. Coreference annotation and resolution in the Colorado Richly Annotated Full Text (CRAFT) corpus of biomedical journal articles. BMC Bioinform. 18:372
    [Google Scholar]
  22. Culotta A, Wick M, McCallum A. 2007. First-order probabilistic models for coreference resolution. Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference81–88. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  23. Daume H III, Marcu D 2005. A large-scale exploration of effective global features for a joint entity detection and tracking model. Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing97–104. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  24. Denis P, Baldridge J. 2007. Joint determination of anaphoricity and coreference resolution using integer programming. Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference236–43. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  25. Devlin J, Chang M-W, Lee K, Toutanova K 2019. BERT: pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)4171–86. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  26. Doddington G, Mitchell A, Przybocki M, Ramshaw L, Strassell S, Weischedel R. 2000. The Automatic Content Extraction (ACE) program—tasks, data, and evaluation. Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC 2004)837–40. Paris: Eur. Lang. Resour. Assoc.
    [Google Scholar]
  27. Durrett G, Klein D. 2013. Easy victories and uphill battles in coreference resolution. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing1971–82. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  28. Durrett G, Klein D. 2014. A joint model for entity analysis: coreference, typing, and linking. Trans. Assoc. Comput. Linguist. 2:477–90
    [Google Scholar]
  29. Eschenbach C, Habel C, Herweg M, Rehkämper K. 1989. Remarks on plural anaphora. EACL ’89: Proceedings of the Fourth Conference on European Chapter of the Association for Computational Linguistics161–67. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  30. Fernandes ER, dos Santos CN, Milidiú RL. 2014. Latent trees for coreference resolution. Comput. Linguist. 40:4801–35
    [Google Scholar]
  31. Fox BA. 1987. Discourse Structure and Anaphora Cambridge, UK: Cambridge Univ. Press
  32. Garnham A. 2001. Mental Models and the Interpretation of Anaphora New York: Psychol. Press
  33. Grobol L. 2020. Coreference resolution for spoken French PhD Thesis, Univ. Sorbonne Nouv. Paris:
  34. Grosz BJ. 1977. The representation and use of focus in dialogue understanding PhD Thesis, Stanford Univ. Stanford, CA:
  35. Grosz BJ, Joshi AK, Weinstein S. 1995 (1986). Centering: a framework for modeling the local coherence of discourse. Comput. Linguist. 21:2202–25
    [Google Scholar]
  36. Grosz BJ, Sidner CL. 1986. Attention, intention, and the structure of discourse. Comput. Linguist. 12:3175–204
    [Google Scholar]
  37. Gundel JK, Abbott B, eds. 2019. The Oxford Handbook of Reference Oxford, UK: Oxford Univ. Press
  38. Heim I. 1982. The semantics of definite and indefinite noun phrases PhD Thesis, Univ. Mass. Amherst:
  39. Hendrickx I, Bouma G, Coppens F, Daelemans W, Hoste V et al. 2008. A coreference corpus and resolution system for Dutch. Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC 2008)144–49. Paris: Eur. Lang. Resour. Assoc.
    [Google Scholar]
  40. Hinrichs EW, Kübler S, Naumann K. 2005. A unified representation for morphological, syntactic, semantic and referential annotations. Proceedings of the Workshop on Frontiers in Corpus Annotation II: Pie in the Sky13–20. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  41. Hobbs JR. 1978. Resolving pronoun references. Lingua 44:311–38
    [Google Scholar]
  42. Hobbs JR, Stickel M, Appelt D, Martin P. 1993. Interpretation as abduction. Artif. Intell. J. 63:69–142
    [Google Scholar]
  43. Hoste V. 2016. The mention-pair model. See Poesio et al. 2016b 269–82
  44. Hou Y, Markert K, Strube M. 2013. Global inference for bridging anaphora resolution. Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies907–17. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  45. Hou Y, Markert K, Strube M. 2018. Unrestricted bridging resolution. Comput. Linguist. 44:2237–84
    [Google Scholar]
  46. Humphreys K, Gaizauskas R, Azzam S, Huyck C, Mitchell B et al. 1998. University of Sheffield: description of the LaSIE-II System as used for MUC-7 Paper presented at the Seventh Message Understanding Conference (MUC-7) Fairfax, VA: Apr. 29–May 1
  47. Ide N, Pustejovsky J, eds. 2017. The Handbook of Linguistic Annotation Dordrecht, Neth: Springer
  48. Iida R, Inui K, Matsumoto Y. 2007. Zero-anaphora resolution by learning rich syntactic pattern features. ACM Trans. Asian Lang. Inf. Process. 6:41
    [Google Scholar]
  49. Iida R, Poesio M. 2011. A cross-lingual ILP solution to zero anaphora resolution. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies804–13. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  50. Joshi M, Chen D, Liu Y, Weld DS, Zettlemoyer L, Levy O. 2020. SpanBERT: improving pre-training by representing and predicting spans. Trans. Assoc. Comput. Linguist. 8:64–77
    [Google Scholar]
  51. Joshi M, Levy O, Zettlemoyer L, Weld D. 2019. BERT for coreference resolution: baselines and analysis. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)5803–8. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  52. Kameyama M. 1985. Zero anaphora: the case of Japanese PhD Thesis, Stanford Univ. Stanford, CA:
  53. Kameyama M. 1997. Recognizing referential links: an information extraction perspective. Proceedings of the ACL Workshop on Operational Factors in Practical, Robust Anaphora Resolution for Unrestricted Texts46–53. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  54. Kamp H, Reyle U. 1993. From Discourse to Logic Dordrecht, Neth: D. Reidel
  55. Kamp H, von Genabith J, Reyle U 2011. Discourse representation theory. Handbook of Philosophical Logic D Gabbay, F Guenthner 125–394. Dordrecht, Neth: Springer
    [Google Scholar]
  56. Kantor B, Globerson A. 2019. Coreference resolution with entity equalization. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics673–77. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  57. Karttunen L 1976. Discourse referents. Syntax and Semantics, Vol. 7: Notes from the Linguistic Underground J McCawley 363–85. New York: Academic
    [Google Scholar]
  58. Kehler A, Appelt D, Taylor L, Simma A. 2004. The (non)utility of predicate-argument frequencies for pronoun interpretation. Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004289–96. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  59. Khosla S, Yu J, Manuvinakurike R, Ng V, Poesio M et al. 2021. The CODI-CRAC 2021 shared task on anaphora, bridging, and discourse deixis in dialogue. Proceedings of the CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue1–15. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  60. Kobayashi H, Ng V. 2020. Bridging resolution: a survey of the state of the art. Proceedings of the 28th International Conference on Computational Linguistics3708–21. n.p.: Int. Comm. Comput. Linguist.
    [Google Scholar]
  61. Kocijan V, Cretu AM, Camburu OM, Yordanov Y, Lukasiewicz T. 2019. A surprisingly robust trick for the Winograd Schema Challenge. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics4837–42. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  62. Kocijan V, Lukasiewicz T, Davis E, Marcus G, Morgenstern L. 2020. A review of Winograd Schema Challenge datasets and approaches. arxiv:2004.13831 [cs.CL]
  63. Kolhatkar V, Roussel A, Dipper S, Zinsmeister H. 2018. Anaphora with non-nominal antecedents in computational linguistics: a survey. Comput. Linguist. 44:3547–612
    [Google Scholar]
  64. Kolhatkar V, Zinsmeister H, Hirst G. 2013. Interpreting anaphoric shell nouns using antecedents of cataphoric shell nouns as training data. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing300–10. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  65. Landragin F. 2016. Description, modéelisation et détection automatique des chaînes de réeférence (DEMOCRAT). Bull. Assoc. Fr. Intell. Artif. 92:11–15
    [Google Scholar]
  66. Lappin S, Leass HJ. 1994. An algorithm for pronominal anaphora resolution. Comput. Linguist. 20:4535–62
    [Google Scholar]
  67. Lee H, Chang A, Peirsman Y, Chambers N, Surdeanu M, Jurafsky D. 2013. Deterministic coreference resolution based on entity-centric, precision-ranked rules. Comput. Linguist. 39:4885–916
    [Google Scholar]
  68. Lee K, He L, Lewis M, Zettlemoyer LS. 2017. End-to-end neural coreference resolution. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing188–97. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  69. Lee K, He L, Zettlemoyer LS. 2018. Higher-order coreference resolution with coarse-to-fine inference. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)687–92. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  70. Levesque HJ, Davis E, Morgenstern L 2012. The Winograd Schema Challenge. KR’12: Proceedings of the Thirteenth International Conference on Principles of Knowledge Representation and Reasoning G Brewka, T Eiter, SA McIlraith 552–61. Palo Alto, CA: AAAI Press
    [Google Scholar]
  71. Luo X. 2005. On coreference resolution performance metrics. Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing25–32. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  72. Luo X, Ittycheriah A, Jing H, Kambhatla N, Roukos S. 2004. A mention-synchronous coreference resolution algorithm based on the Bell tree. Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)135–42. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  73. Luo X, Pradhan S. 2016. Evaluation metrics. See Poesio et al. 2016b 141–63
  74. Lyons J. 1977. Semantics Cambridge, UK: Cambridge Univ. Press
  75. Mann WC, Thompson SA. 1988. Rhetorical Structure Theory: toward a functional theory of text organization. Text 8:3243–81
    [Google Scholar]
  76. Marasović A, Born L, Opitz J, Frank A. 2017. A mention-ranking model for abstract anaphora resolution. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing221–32. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  77. Markert K, Hou Y, Strube M. 2012. Collective classification for fine-grained information status. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)795–804. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  78. Markert K, Nissim M. 2005. Comparing knowledge sources for nominal anaphora resolution. Comput. Linguist. 31:3367–402
    [Google Scholar]
  79. Martschat S, Strube M. 2015. Latent structures for coreference resolution. Trans. Assoc. Comput. Linguist. 3:405–18
    [Google Scholar]
  80. McEnery A, Tanaka I, Botley S 1997. Corpus annotation and reference resolution. ANARESOLUTION ’97: Proceedings of a Workshop on Operational Factors in Practical, Robust Anaphora Resolution for Unrestricted Texts R Mitkov, B Boguraev 67–74. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  81. Mikolov T, Sutskever I, Chen K, Corrado G, Dean J. 2013. Distributed representations of words and phrases and their compositionality. NIPS’13: Proceedings of the 26th International Conference on Neural Information Processing Systems, Vol. 23111–19. Red Hook, NY: Curran
    [Google Scholar]
  82. Mitkov R. 1998. Robust pronoun resolution with limited knowledge. Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Vol. 2869–75. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  83. Mitkov R. 2002. Anaphora Resolution London: Longman
  84. Müller MC. 2008. Fully automatic resolution of it, this and that in unrestricted multy-party dialog PhD Thesis, Univ. Tübingen Tübingen, Ger:.
    [Google Scholar]
  85. Muzerelle J, Lefeuvre A, Schang E, Antoine JY, Pelletier A et al. 2014. Ancor_centre, a large free spoken French coreference corpus: description of the resource and reliability measures. Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2014)843–47. Paris: Eur. Lang. Resour. Assoc.
    [Google Scholar]
  86. Nedoluzhko A, Mírovský J, Ocelák R, Pergler J. 2009. Extended coreferential relations and bridging anaphora in the Prague Dependency Treebank Presented at the 7th Discourse Anaphora and Anaphor Resolution Colloquium (DAARC 2009) Goa, India: Nov. 5–6
  87. Nedoluzhko A, Novák M, Popel M, abokrtský Z, Zeman D. 2021. Coreference meets universal dependencies—a pilot experiment on harmonizing coreference datasets for 11 languages ÚFAL Tech. Rep. TR-2021-66 Charles Univ. Prague:
  88. Ng V. 2016. Advanced machine learning models for coreference resolution. See Poesio et al. 2016b 283–313
  89. Ng V, Cardie C. 2002a. Identifying anaphoric and non-anaphoric noun phrases to improve coreference resolution. COLING 2002: The 19th International Conference on Computational Linguistics n.p.: Int. Comm. Comput. Linguist. https://aclanthology.org/C02-1139/
    [Google Scholar]
  90. Ng V, Cardie C. 2002b. Improving machine learning approaches to coreference resolution. Proceedings of the 40th Meeting of the Association for Computational Linguistics104–11. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  91. Nguyen NLT, Kim JD, Tsujii J. 2011. Overview of the protein coreference task in BioNLP shared task. Proceedings of the BioNLP Shared Task Workshop74–82. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  92. Nicolae C, Nicolae G. 2006. BESTCUT: a graph algorithm for coreference resolution. Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing275–83. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  93. Ogrodniczuk M, Głowinśka K, Kopeć M, Savary A, Zawisławska M. 2015. Coreference in Polish: Annotation, Resolution and Evaluation Berlin: Walter de Gruyter
  94. Partee BH 1972. Opacity, coreference, and pronouns. Semantics for Natural Language D Davidson, G Harman 415–41. Dordrecht, Neth: D. Reidel
    [Google Scholar]
  95. Passonneau RJ. 1997. Instructions for applying discourse reference annotation for multiple applications (DRAMA) Work. Pap., Columbia Univ. New York:
  96. Peters ME, Neumann M, Iyyer M, Gardner M, Clark C et al. 2018. Deep contextualized word representations. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)2227–37. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  97. Poesio M. 1994. Discourse interpretation and the scope of operators PhD Thesis, Univ. Rochester Rochester, NY:
  98. Poesio M. 2004. Discourse annotation and semantic annotation in the GNOME corpus. Proceedings of the Workshop on Discourse Annotation72–79. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  99. Poesio M. 2016. Linguistic and cognitive evidence about anaphora. See Poesio et al. 2016b 23–54
  100. Poesio M, Artstein R 2005. The reliability of anaphoric annotation, reconsidered: taking ambiguity into account. Proceedings of the Workshop on Frontiers in Corpus Annotations II: Pie in the Sky A Meyers 76–83. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  101. Poesio M, Artstein R. 2008. Anaphoric annotation in the ARRAU corpus. Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC 2008)1170–74. Paris: Eur. Lang. Resour. Assoc.
    [Google Scholar]
  102. Poesio M, Bruneseaux F, Romary L 1999. The MATE meta-scheme for coreference in dialogues in multiple languages. Proceedings of the ACL Workshop on Standards and Tools for Discourse Tagging M Walker 65–74. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  103. Poesio M, Chamberlain J, Kruschwitz U, Paun S, Uma A, Yu J. 2019. A crowdsourced corpus of multiple judgments and disagreement on anaphoric interpretation. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)1778–89. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  104. Poesio M, Grishina Y, Kolhatkar V, Moosavi N, Roesiger I et al. 2018. Anaphora resolution with the ARRAU corpus. Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference11–22. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  105. Poesio M, Mehta R, Maroudas A, Hitzeman J. 2004a. Learning to resolve bridging references. Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)143–50. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  106. Poesio M, Pradhan S, Recasens M, Rodriguez K, Versley Y. 2016a. Annotated corpora and annotation tools. See Poesio et al. 2016b 97–140
  107. Poesio M, Stevenson R, Di Eugenio B, Hitzeman JM. 2004b. Centering: a parametric theory and its instantiations. Comput. Linguist. 30:3309–63
    [Google Scholar]
  108. Poesio M, Stuckardt R, Versley Y. 2016b. Anaphora Resolution: Algorithms, Resources and Applications Berlin: Springer
  109. Poesio M, Stuckardt R, Versley Y, Vieira R. 2016c. Early approaches to anaphora resolution: theoretically inspired and heuristic-based. See Poesio et al. 2016b 55–94
  110. Poesio M, Vieira R. 1998. A corpus-based investigation of definite description use. Comput. Linguist. 24:2183–216
    [Google Scholar]
  111. Ponzetto S, Strube M. 2006. Exploiting semantic role labeling, WordNet and Wikipedia for coreference resolution. Proceedings of the Human Language Technology Conference of the NAACL, Main Conference192–99. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  112. Pradhan S, Luo X, Recasens M, Hovy E, Ng V, Strube M. 2014. Scoring coreference partitions of predicted mentions: a reference implementation. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)30–35. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  113. Pradhan S, Moschitti A, Xue N, Uryupina O, Zhang Y. 2012. CoNLL-2012 shared task: modeling multilingual unrestricted coreference in OntoNotes. Proceedings of the Joint Conference on EMNLP and CoNLL - Shared Task1–40. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  114. Rahman A, Ng V. 2011. Narrowing the modeling gap: a cluster-ranking approach to coreference resolution. J. Artif. Intell. Res. 40:469–521
    [Google Scholar]
  115. Rahman A, Ng V. 2012. Resolving complex cases of definite pronouns: the Winograd Schema Challenge. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning777–89. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  116. Recasens M, Hovy E, Martí MA. 2011. Identity, non-identity, and near-identity: addressing the complexity of coreference. Lingua 121:61138–52
    [Google Scholar]
  117. Recasens M, Màrquez L, Sapena E, Martí MA, Taulé M et al. 2010. SemEval-2010 Task 1: coreference resolution in multiple languages. Proceedings of the 5th International Workshop on Semantic Evaluation (SemEval 2010)1–8. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  118. Recasens M, Martí MA. 2010. AnCora-CO: coreferentially annotated corpora for Spanish and Catalan. Lang. Resourc. Eval. 44:4315–45
    [Google Scholar]
  119. Recasens M, Pradhan S. 2016. Evaluation campaigns. See Poesio et al. 2016b 165–208
  120. Rizzolo N, Roth D. 2016. Integer linear programming for coreference resolution. See Poesio et al. 2016b 315–43
  121. Roesiger I, Riester A, Kuhn J. 2018. Bridging resolution: task definition, corpus resources and rule-based experiments. Proceedings of the 27th International Conference on Computational Linguistics3516–28. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  122. Sakaguchi K, Le Bras R, Bhagavatula C, Choi Y 2020. WINOGRANDE: an adversarial Winograd Schema Challenge at scale. The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20)8732–34. Palo Alto, CA: AAAI Press
    [Google Scholar]
  123. Sanford AJ, Garrod SC. 1981. Understanding Written Language Chichester, UK: Wiley
  124. Sasano R, Kawahara D, Kurohashi S. 2009. The effect of corpus size on case frame acquisition for discourse analysis. Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics521–29. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  125. Sidner CL. 1979. Towards a computational theory of definite anaphora comprehension in English discourse PhD Thesis, MIT Cambridge, MA:
  126. Soon WM, Lim DCY, Ng HT. 2001. A machine learning approach to coreference resolution of noun phrases. Comput. Linguist. 27:4521–44
    [Google Scholar]
  127. Tetreault JR. 2001. A corpus-based evaluation of Centering and pronoun resolution. Comput. Linguist. 27:4507–20
    [Google Scholar]
  128. Uryupina O, Artstein R, Bristot A, Cavicchio F, Delogu F et al. 2020. Annotating a broad range of anaphoric phenomena, in a variety of genres: the ARRAU corpus. J. Nat. Lang. Eng. 26:195–128
    [Google Scholar]
  129. Uryupina O, Kabadjov MA, Poesio M. 2016. Detecting non-reference and non-anaphoricity. See Poesio et al. 2016b 369–92
  130. Vala H, Piper A, Ruths D. 2016. The more antecedents, the merrier: resolving multi-antecedent anaphors. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)2287–96. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  131. van Deemter K, Kibble R. 2000. On coreferring: coreference in MUC and related annotation schemes. Comput. Linguist. 26:4629–37
    [Google Scholar]
  132. Versley Y, Poesio M, Ponzetto SP. 2016. Using lexical and encyclopedic knowledge. See Poesio et al. 2016b 393–429
  133. Vieira R, Poesio M. 2000. An empirically-based system for processing definite descriptions. Comput. Linguist. 26:4539–93
    [Google Scholar]
  134. Vilain M, Burger J, Aberdeen J, Connolly D, Hirschman L 1995. A model-theoretic coreference scoring scheme. Proceedings of the Sixth Message Understanding Conference (MUC-6)45–52. San Francisco: Morgan Kaufmann
    [Google Scholar]
  135. Walker MA, Joshi AK, Prince EF, eds. 1998. Centering Theory in Discourse Oxford, UK: Clarendon
  136. Wang A, Singh A, Michael J, Hill F, Levy O, Bowman S. 2019. GLUE: a multi-task benchmark and analysis platform for natural language understanding. Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP353–55. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  137. Webber BL. 1979. A Formal Approach to Discourse Anaphora New York: Garland
  138. Webber BL. 1991. Structure and ostension in the interpretation of discourse deixis. Lang. Cogn. Process. 6:2107–35
    [Google Scholar]
  139. Webster K, Recasens M, Axelrod V, Baldridge J. 2018. Mind the GAP: a balanced corpus of gendered ambiguous pronouns. Trans. Assoc. Comput. Linguist. 6:605–17
    [Google Scholar]
  140. Winograd T. 1972. Understanding natural language. Cogn. Psychol. 3:11–191
    [Google Scholar]
  141. Wiseman SJ, Rush AM, Shieber SM, Weston J 2015. Learning anaphoricity and antecedent ranking features for coreference resolution. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)1416–26. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  142. Xia P, Durme BV. 2021. Moving on from OntoNotes: coreference resolution model transfer. Proceedings of the Conference on Empirical Methods in Natural Language Processing5241–56. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  143. Yang X, Zhou G, Su J, Tan CL 2004. Improving noun phrase coreference resolution by matching strings. Lecture Notes in Computer Science, Vol. 3248: Natural Language Processing—IJCNLP 2004 KY Su, J Tsujii, JH Lee, OY Kwong 22–31. Berlin: Springer
    [Google Scholar]
  144. Yu J, Bohnet B, Poesio M. 2020a. Neural mention detection. Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020)1–10. Paris: Eur. Lang. Resour. Assoc.
    [Google Scholar]
  145. Yu J, Khosla S, Moosavi N, Paun S, Pradhan S, Poesio M. 2022. The Universal Anaphora scorer. Proceedings of the Thirteenth Language Resources and Evaluation Conference (LREC 2022)4873–83. Paris: Eur. Lang. Resour. Assoc.
    [Google Scholar]
  146. Yu J, Moosavi N, Paun S, Poesio M. 2021. Stay together: a system for single and split-antecedent anaphora resolution. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies4174–84. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  147. Yu J, Moosavi NS, Paun S, Poesio M. 2020b. Free the plural: unrestricted split-antecedent anaphora resolution. Proceedings of the 28th International Conference on Computational Linguistics6113–25. n.p.: Int. Comm. Comput. Linguist.
    [Google Scholar]
  148. Yu J, Poesio M. 2020. Multitask learning-based neural bridging reference resolution. Proceedings of the 28th International Conference on Computational Linguistics3534–46. n.p.: Int. Comm. Comput. Linguist.
    [Google Scholar]
  149. Yu J, Uma A, Poesio M 2020c. A cluster ranking model for full anaphora resolution. Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020)11–20. Paris: Eur. Lang. Resour. Assoc.
    [Google Scholar]
  150. Zaenen A. 2006. Mark-up barking up the wrong tree. Comput. Linguist. 32:4577–80
    [Google Scholar]
  151. Zeldes A. 2020. Multilayer Corpus Studies New York/London: Routledge
  152. Zeldes A. 2022. Can we fix the scope for coreference?. Dialogue Discourse 13:141–62
    [Google Scholar]
  153. Zhang H, Song Y, Song Y, Yu D 2019. Knowledge-aware pronoun coreference resolution. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics867–76. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
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