An algorithm for pronominal anaphora resolution
Computational Linguistics
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Discovery of inference rules for question-answering
Natural Language Engineering
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Evaluating automated and manual acquisition of anaphora resolution strategies
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Automatic processing of large corpora for the resolution of anaphora references
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
Evaluation tool for rule-based anaphora resolution methods
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Improving pronoun resolution using statistics-based semantic compatibility information
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
ANARESOLUTION '97 Proceedings of a Workshop on Operational Factors in Practical, Robust Anaphora Resolution for Unrestricted Texts
An expectation maximization approach to pronoun resolution
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Automatic acquisition of gender information for anaphora resolution
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
The Mitkov Algorithm for Anaphora Resolution in Portuguese
PROPOR '08 Proceedings of the 8th international conference on Computational Processing of the Portuguese Language
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
Chinese pronominal anaphora resolution using lexical knowledge and entropy-based weight
Journal of the American Society for Information Science and Technology
Glen, Glenda or Glendale: unsupervised and semi-supervised learning of English noun gender
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Exploring domain differences for the design of pronoun resolution systems for biomedical text
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Shallow semantics for coreference resolution
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Simple training of dependency parsers via structured boosting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Pronoun resolution with Markov logic networks
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Supervised noun phrase coreference research: the first fifteen years
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A multi-pass sieve for coreference resolution
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Antelogue: pronoun resolution for text and dialogue
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations
CoNLL-2011 shared task: modeling unrestricted coreference in OntoNotes
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
RelaxCor participation in CoNLL shared task on coreference resolution
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
Exploring lexicalized features for coreference resolution
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
Rule and tree ensembles for unrestricted coreference resolution
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
Combining syntactic and semantic features by SVM for unrestricted coreference resolution
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
Hybrid approach for coreference resolution
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
Link type based pre-cluster pair model for coreference resolution
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
Stylometric analysis of scientific articles
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Coreference semantics from web features
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Resolving complex cases of definite pronouns: the winograd schema challenge
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
A mixed deterministic model for coreference resolution
CoNLL '12 Joint Conference on EMNLP and CoNLL - Shared Task
A multigraph model for coreference resolution
CoNLL '12 Joint Conference on EMNLP and CoNLL - Shared Task
Deterministic coreference resolution based on entity-centric, precision-ranked rules
Computational Linguistics
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We present an approach to pronoun resolution based on syntactic paths. Through a simple bootstrapping procedure, we learn the likelihood of coreference between a pronoun and a candidate noun based on the path in the parse tree between the two entities. This path information enables us to handle previously challenging resolution instances, and also robustly addresses traditional syntactic coreference constraints. Highly coreferent paths also allow mining of precise probabilistic gender/number information. We combine statistical knowledge with well known features in a Support Vector Machine pronoun resolution classifier. Significant gains in performance are observed on several datasets.