A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
An algorithm for pronominal anaphora resolution
Computational Linguistics
Centering: a framework for modeling the local coherence of discourse
Computational Linguistics
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
A trainable approach to coreference resolution for information extraction
A trainable approach to coreference resolution for information extraction
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Parameter optimization for machine-learning of word sense disambiguation
Natural Language Engineering
Providing a unified account of definite noun phrases in discourse
ACL '83 Proceedings of the 21st 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
Anaphora resolution: a multi-strategy approach
COLING '88 Proceedings of the 12th conference on Computational linguistics - Volume 1
Anaphora for everyone: pronominal anaphora resoluation without a parser
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Description of the UMass system as used for MUC-6
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
Coreference resolution using competition learning approach
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
The influence of minimum edit distance on reference resolution
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
CogNIAC: high precision coreference with limited knowledge and linguistic resources
ANARESOLUTION '97 Proceedings of a Workshop on Operational Factors in Practical, Robust Anaphora Resolution for Unrestricted Texts
Using decision trees for conference resolution
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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The aim of this paper is twofold. On the one hand, it attempts to explore several machine learning models for pronoun resolution in Turkish, a language not sufficiently studied with respect to anaphora resolution and rarely being subjected to machine learning experiments. On the other hand, this paper offers an evaluation of the classification performances of the learning models in order to gain insight into the question of how to match a model to the task at hand. In addition to the expected observation that each model should be tuned to an optimum level of expressive power so as to avoid underfitting and overfitting, the results also suggest that non-linear models properly tuned to avoid overfitting outperform linear ones when applied to the data used in our experiments.