Making large-scale support vector machine learning practical
Advances in kernel methods
The syntactic process
Kernel methods for relation extraction
The Journal of Machine Learning Research
An empirically based system for processing definite descriptions
Computational Linguistics
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Never look back: an alternative to centering
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
Corpus-based identification of non-anaphoric noun phrases
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Identifying anaphoric and non-anaphoric noun phrases to improve coreference resolution
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Ranking algorithms for named-entity extraction: boosting and the voted perceptron
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
High-precision identification of discourse new and unique noun phrases
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
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
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
On coreference resolution performance metrics
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Coreference systems based on kernels methods
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Learning information status of discourse entities
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Specialized models and ranking for coreference resolution
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Graph-cut-based anaphoricity determination for coreference resolution
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Using decision trees for conference resolution
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Supervised models for coreference resolution
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Global learning of noun phrase anaphoricity in coreference resolution via label propagation
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Language Resources and Evaluation
Learning the fine-grained information status of discourse entities
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Collective classification for fine-grained information status
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Automatically acquiring fine-grained information status distinctions in German
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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An entity in a dialogue may be old, new, or mediated/inferrable with respect to the hearer's beliefs. Knowing the information status of the entities participating in a dialogue can therefore facilitate its interpretation. We address the under-investigated problem of automatically determining the information status of discourse entities. Specifically, we extend Nissim's (2006) machine learning approach to information-status determination with lexical and structured features, and exploit learned knowledge of the information status of each discourse entity for coreference resolution. Experimental results on a set of Switchboard dialogues reveal that (1) incorporating our proposed features into Nissim's feature set enables our system to achieve state-of-the-art performance on information-status classification, and (2) the resulting information can be used to improve the performance of learning-based coreference resolvers.