STOP WORD DETECTION AS A BINARY CLASSIFICATION PROBLEM

Year 2017, Volume: 18 Issue: 2, 346 - 359, 30.06.2017

Senem Kumova Metin , Bahar Karaoğlan

https://doi.org/10.18038/aubtda.322136

Cited By: 2

Abstract

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.

Keywords

stop word, content word, binary classification, tf-idf