Impact of Feature Selection for Corpus-Based WSD in Turkish

Orhan, Zeynep; Altan, Zeynep

doi:10.1007/11925231_83

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…In consequence, it has enjoyed substantial uptake (Nikora 2005;Camporelli 2006;Flikka et al 2006;Orhan and Altan 2006;Lasko et al 2006;Hunt 2006;Ferrari and Aitken 2006;Birzele and Kramer 2006;Kunchevaa et al 2007;Lau et al 2007;Masegosa et al 2007;Wang et al 2007;Garcia et al 2008;Tian et al 2008;Kurz et al 2009;Leon et al 2009;Shahri and Jamil 2009;Simpson et al 2009;Affendey et al 2010;García-Jiménez et al 2010;Hopfgartner et al 2010;Liew et al 2010). …”

Section: Aodementioning

confidence: 99%

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

et al. 2011

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Averaged n-Dependence Estimators (AnDE) is an approach to probabilistic classification learning that learns by extrapolation from marginal to full-multivariate probability distributions. It utilizes a single parameter that transforms the approach between a lowvariance high-bias learner (Naive Bayes) and a high-variance low-bias learner with Bayes optimal asymptotic error. It extends the underlying strategy of Averaged One-Dependence Estimators (AODE), which relaxes the Naive Bayes independence assumption while retaining many of Naive Bayes' desirable computational and theoretical properties. AnDE further relaxes the independence assumption by generalizing AODE to higher-levels of dependence. Extensive experimental evaluation shows that the bias-variance trade-off for Averaged 2-Dependence Estimators results in strong predictive accuracy over a wide range of data sets. It has training time linear with respect to the number of examples, learns in a single pass through the training data, supports incremental learning, handles directly missing values, and is robust in the face of noise. Beyond the practical utility of its lower-dimensional variants, AnDE is of interest in that it demonstrates that it is possible to create low-bias high-variance generative learners and suggests strategies for developing even more powerful classifiers.

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Section: Aodementioning

confidence: 99%

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

et al. 2011

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“…Then tab delimited text files including structural and sense tag information are obtained. In these files each line has contextual information that are thought to be effective (Orhan and Altan, 2006;Orhan and Altan, 2005) in Turkish WSD about the target words. In the upper level for each of them XML file id, sentence number and the order of the ambiguous word are kept as a unique key for that specific target.…”

Section: Training and Evaluation Datamentioning

confidence: 99%

SemEval-2007 task 12

Orhan

Çelik

Neslihan

2007

Proceedings of the 4th International Workshop on Semantic Evaluations - SemEval '07

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This paper presents the task definition, resources, and the single participant system for Task 12: Turkish Lexical Sample Task (TLST), which was organized in the Se-mEval-2007 evaluation exercise. The methodology followed for developing the specific linguistic resources necessary for the task has been described in this context. A language-specific feature set was defined for Turkish. TLST consists of three pieces of data: The dictionary, the training data, and the evaluation data. Finally, a single system that utilizes a simple statistical method was submitted for the task and evaluated.

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A multilayer annotated corpus for Turkish

Yıldız

Ercan

et al. 2018

2018 2nd International Conference on Natural Language and Speech Processing (ICNLSP)

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Impact of Feature Selection for Corpus-Based WSD in Turkish

Cited by 4 publications

References 2 publications

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

SemEval-2007 task 12

A multilayer annotated corpus for Turkish

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