No abstract

No abstract

In this paper, we present a stochastic part-of-speech tagger for Turkish. The tagger is primarily developed for information retrieval purposes, but it can as well serve as a light-weight PoS tagger for other purposes. The tagger uses a well-established Hidden Markov model of the language with a closed lexicon that consists of fixed number of letters from the word endings. We have considered seven different lengths of word endings against 30 training corpus sizes. Bestcase accuracy obtained is 90.2% with 5 characters. The main contribution of this paper is to present a way of constructing a closed vocabulary for part-of-speech tagging effort that can be useful for highly inflected languages like Turkish, Finnish, Hungarian, Estonian, and Czech.

In this chapter, the authors first discuss how Roger’s theory of innovation diffusion can be incorporated into ICTs in formal and informal learning and teaching environments. The authors begin by presenting the use of ICT in education in general terms, then they introduce Rogers’ diffusion of innovation (DoI) theory and the related literature. This is followed by a description of a project which explored the relationship between some characteristics of primary science teachers and their attitudes toward the use of ICT in education. A national project was funded by the Scientific and Technological Research Council of Turkey (TÜBITAK), and Ege University, Science and Technology Application and Research Center. The last section involves a discussion of the diffusion of technological innovations into science education in the light of Rogers’ DoI theory.

In a wide group of languages, the stop words, which have only grammatical roles and not contributing to information content, may be simply exposed by their relatively higher occurrence frequencies. But, in agglutinative or inflectional languages, a stop word may be observed in several different surface forms due to the inflection producing noise.In this study, some of the well-known binary classification methods are employed to overcome the inflectional noise problem in stop word detection. The experiments are conducted on corpora of an agglutinative language, Turkish, in which the amount of inflection is high and a non-agglutinative language, English, in which the inflection is lower for stop words. The evaluations demonstrated that in Turkish corpus, the classification methods improve stop word detection with respect to frequency-based method. On the other hand, the classification methods applied on English corpora showed no improvement in the performance of stop word detection.