Methods and algorithms for unsupervised learning of morphology

This is an accepted manuscript of a chapter published by Springer in Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8403 in 2014 available online: https://doi.org/10.1007/978-3-642-54906-9_15 The accepted version of the publication may differ from the final published version.This paper is a survey of methods and algorithms for unsupervised learning of morphology. We provide a description of the methods and algorithms used for morphological segmentation from a computational linguistics point of view. We survey morphological segmentation methods covering methods based on MDL (minimum description length), MLE (maximum likelihood estimation), MAP (maximum a posteriori), parametric and non-parametric Bayesian approaches. A review of the evaluation schemes for unsupervised morphological segmentation is also provided along with a summary of evaluation results on the Morpho Challenge evaluations.Published versio

Turkish information retrieval: Past changes future

One of the most exciting accomplishments of computer science in the lifetime of this generation is the World Wide Web. The Web is a global electronic publishing medium. Its size has been growing with an enormous speed for over a decade. Most of its content is objectionable, but it also contains a huge amount of valuable information. The Web adds a new dimension to the concept of information explosion and tries to solve the very same problem by information retrieval systems known as Web search engines. We briefly review the information explosion problem and information retrieval systems, convey the past and state of the art in Turkish information retrieval research, illustrate some recent developments, and propose some future actions in this research area in Turkey. © Springer-Verlag Berlin Heidelberg 2006