Interpreting anaphors in natural language texts
Interpreting anaphors in natural language texts
An algorithm for pronominal anaphora resolution
Computational Linguistics
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Robust pronoun resolution with limited knowledge
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
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
Anaphora resolution: a multi-strategy approach
COLING '88 Proceedings of the 12th conference on Computational linguistics - Volume 1
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Chinese Pronominal Anaphora Resolution Based on Conditional Random Fields
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 01
The crotal SRL system: a generic tool based on tree-structured CRF
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
Control of inference: role of some aspects of discourse structure-centering
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
Centered logic: the role of entity centered sentence representation in natural language inferencing
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 1
Using decision trees for conference resolution
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A Deeper Look into Features for Coreference Resolution
DAARC '09 Proceedings of the 7th Discourse Anaphora and Anaphor Resolution Colloquium on Anaphora Processing and Applications
Semantic and syntactic features for dutch coreference resolution
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Supervised noun phrase coreference research: the first fifteen years
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Coreference resolution with reconcile
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
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
Hybrid approach for coreference resolution
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
Learning to model multilingual unrestricted coreference in OntoNotes
CoNLL '12 Joint Conference on EMNLP and CoNLL - Shared Task
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Coreference resolution is the task of identifying which noun phrases or mentions refer to the same real-world entity in a text or a dialogue. This is an essential task in many of the NLP applications such as information extraction, question answering system, summarization, machine translation and in information retrieval systems. Coreference Resolution is traditionally considered as pairwise classification problem and different classification techniques are used to make a local classification decision. We are using Tree-CRF for this task. With Tree-CRF we make a joint prediction of the anaphor and the antecedent. Tree-based Reparameterization (TRP) for approximate inference is used for the parameter learning. TRP performs an exact computation over the spanning trees of a full graph. This helps in learning the long distance dependency. The approximate inference methodology does a better convergence. We have used the parsed tree from the OntoNotes, released for CoNLL shared task 2011. We derive features from the parse tree. We have used the different genre data for the experiments. The results are encouraging.