Mehmet Ali Yatbaz


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Learning grammatical categories using paradigmatic representations: Substitute words for language acquisition
Mehmet Ali Yatbaz | Volkan Cirik | Aylin Küntay | Deniz Yuret
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Learning syntactic categories is a fundamental task in language acquisition. Previous studies show that co-occurrence patterns of preceding and following words are essential to group words into categories. However, the neighboring words, or frames, are rarely repeated exactly in the data. This creates data sparsity and hampers learning for frame based models. In this work, we propose a paradigmatic representation of word context which uses probable substitutes instead of frames. Our experiments on child-directed speech show that models based on probable substitutes learn more accurate categories with fewer examples compared to models based on frames.


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Unsupervised Instance-Based Part of Speech Induction Using Probable Substitutes
Deniz Yuret | Mehmet Ali Yatbaz | Enis Sert
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers


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Learning Syntactic Categories Using Paradigmatic Representations of Word Context
Mehmet Ali Yatbaz | Enis Sert | Deniz Yuret
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning


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Unsupervised Part of Speech Tagging Using Unambiguous Substitutes from a Statistical Language Model
Mehmet Ali Yatbaz | Deniz Yuret
Coling 2010: Posters

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The Noisy Channel Model for Unsupervised Word Sense Disambiguation
Deniz Yuret | Mehmet Ali Yatbaz
Computational Linguistics, Volume 36, Number 1, March 2010


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Discriminative vs. Generative Approaches in Semantic Role Labeling
Deniz Yuret | Mehmet Ali Yatbaz | Ahmet Engin Ural
CoNLL 2008: Proceedings of the Twelfth Conference on Computational Natural Language Learning