Turkish Wikipedia Named-Entity Recognition and Text Categorization (TWNERTC) dataset is a collection of automatically categorized and annotated sentences obtained from Wikipedia. We constructed large-scale gazetteers by using a graph crawler algorithm to extract relevant entity and domain information from a semantic knowledge base, Freebase. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 77 different domains. Since automated processes are prone to ambiguity, we also introduce two new content specific noise reduction methodologies. Moreover, we map fine-grained entity types to the equivalent four coarse-grained types: person, loc, org, misc. Eventually, we construct six different dataset versions and evaluate the quality of annotations by comparing ground truths from human annotators. We make these datasets publicly available to support studies on Turkish named-entity recognition (NER) and text categorization (TC).
Morphologically rich languages such as Turkish may benefit from morphological analysis in natural language tasks. In this study, we examine the effects of morphological analysis on text categorization task in Turkish. We use stems and word categories that are extracted with morphological analysis as main features and compare them with fixed length stemmers in a bag of words approach with several learning algorithms. We aim to show the effects of using varying degrees of morphological information. In this study, a first comprehensive text classification using n-gram model has been realized for Turkish. We worked in 3 different areas such as determining the identification of a Turkish document's author, classifying documents according to text's genre and identifying a gender of an author, automatically. Naive Bayes, Support Vector Machine, C 4.5 and Random Forest were used as classification methods and the results were given comparatively. The success in determining the author of the text, genre of the text and gender of the author was obtained as 83 , 93 and 96 , respectively. In news portals, text category information is needed for news presentation. However, for many news stories the category information is unavailable, incorrectly assigned or too generic. This makes the text categorization a necessary tool for news portals. Automated text categorization (ATC) is a multifaceted difficult process that involves decisions regarding tuning of several parameters, term weighting, word stemming, word stopping, and feature selection. In this study we aim to find a categorization setup that will provide highly accurate results in ATC for Turkish news portals. We also examine some other aspects such as the effects of training dataset set size and robustness issues. Two Turkish test collections with different characteristics are created using Bilkent News Portal. Experiments are conducted with four classification methods: C4.5, KNN, Naive Bayes, and SVM (using polynomial and rbf kernels). Our results recommends a text categorization template for Turkish news portals and provides some future research pointers. 