Introduction to algorithms
Machine Learning
A maximum entropy approach to natural language processing
Computational Linguistics
High performance question/answering
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
ESA '94 Proceedings of the Second Annual European Symposium on Algorithms
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
A model-theoretic coreference scoring scheme
MUC6 '95 Proceedings of the 6th conference on Message understanding
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Learning as search optimization: approximate large margin methods for structured prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
A mention-synchronous coreference resolution algorithm based on the Bell tree
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Machine learning for coreference resolution: from local classification to global ranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
SenseLearner: word sense disambiguation for all words in unrestricted text
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Accurate semantic class classifier for coreference resolution
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Graph-based event coreference resolution
TextGraphs-4 Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing
Using topic themes for multi-document summarization
ACM Transactions on Information Systems (TOIS)
Supervised noun phrase coreference research: the first fifteen years
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Graph-based clustering for computational linguistics: a survey
TextGraphs-5 Proceedings of the 2010 Workshop on Graph-based Methods for Natural Language Processing
Type level clustering evaluation: new measures and a POS induction case study
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
End-to-end coreference resolution via hypergraph partitioning
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Inducing fine-grained semantic classes via hierarchical and collective classification
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Evaluation metrics for end-to-end coreference resolution systems
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Streaming cross document entity coreference resolution
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
A global relaxation labeling approach to coreference resolution
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Journal of Biomedical Informatics
Random walks down the mention graphs for event coreference resolution
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
A constraint-based hypergraph partitioning approach to coreference resolution
Computational Linguistics
| Hi-index | 0.00 |
In this paper we describe a coreference resolution method that employs a classification and a clusterization phase. In a novel way, the clusterization is produced as a graph cutting algorithm, in which nodes of the graph correspond to the mentions of the text, whereas the edges of the graph constitute the confidences derived from the coreference classification. In experiments, the graph cutting algorithm for coreference resolution, called BestCut, achieves state-of-the-art performance.