An algorithm for pronominal anaphora resolution
Computational Linguistics
The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Kernel methods for relation extraction
The Journal of Machine Learning Research
Using the web to obtain frequencies for unseen bigrams
Computational Linguistics - Special issue on web as corpus
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
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
Anaphora for everyone: pronominal anaphora resoluation without a parser
COLING '96 Proceedings of the 16th 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
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Improving pronoun resolution by incorporating coreferential information of candidates
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
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ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Multi-lingual coreference resolution with syntactic features
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Zero-anaphora resolution by learning rich syntactic pattern features
ACM Transactions on Asian Language Information Processing (TALIP)
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
Zero anaphora resolution by case-based reasoning and pattern conceptualization
Expert Systems with Applications: An International Journal
BART: a modular toolkit for coreference resolution
HLT-Demonstrations '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Demo Session
Coreference systems based on kernels methods
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
EM works for pronoun anaphora resolution
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Fast full parsing by linear-chain conditional random fields
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Tree kernel-based SVM with structured syntactic knowledge for BTG-based phrase reordering
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
Comparison of classification and ranking approaches to pronominal anaphora resolution in Czech
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Semantic and syntactic features for dutch coreference resolution
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Convolution kernel over packed parse forest
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Supervised noun phrase coreference research: the first fifteen years
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
A tree kernel-based unified framework for Chinese zero anaphora resolution
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COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Dependency-driven anaphoricity determination for coreference resolution
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
A first machine learning approach to pronominal anaphora resolution in Basque
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Learning noun phrase anaphoricity in coreference resolution via label propagation
Journal of Computer Science and Technology - Special issue on natural language processing
Journal of Biomedical Informatics
Coreference resolution with loose transitivity constraints
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
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ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Improve tree kernel-based event pronoun resolution with competitive information
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Anaphora resolution in biomedical literature: a hybrid approach
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A constraint-based hypergraph partitioning approach to coreference resolution
Computational Linguistics
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Syntactic knowledge is important for pronoun resolution. Traditionally, the syntactic information for pronoun resolution is represented in terms of features that have to be selected and defined heuristically. In the paper, we propose a kernel-based method that can automatically mine the syntactic information from the parse trees for pronoun resolution. Specifically, we utilize the parse trees directly as a structured feature and apply kernel functions to this feature, as well as other normal features, to learn the resolution classifier. In this way, our approach avoids the efforts of decoding the parse trees into the set of flat syntactic features. The experimental results show that our approach can bring significant performance improvement and is reliably effective for the pronoun resolution task.