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    Learning-based pronoun resolution for Turkish with a comparative evaluation
    (Academic Press Ltd- Elsevier Science Ltd, 2009) Kilicaslan, Yilmaz; Guner, Edip Serdar; Yildirim, Savas
    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. (C) 2008 Elsevier Ltd. All rights reserved.
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    Pronoun Resolution in Turkish Using Decision Tree and Rule-Based Learning Algorithms
    (Springer-Verlag Berlin, 2009) Yildirim, Savas; Kilicaslan, Yilmaz; Yildiz, Tugba
    This paper reports on the results of some pronoun resolution experiments performed by applying a decision tree and a rule-based algorithm on an annotated Turkish text. The text has been compiled mostly from various popular child stories in a semi-automatic way. A knowledge-lean learning model has been devised using only nine most commonly employed features. An evaluation and comparison of the performances achieved with the two different algorithms is offered in terms of the recall, precision and f-measure metrics.