Author/Authors
pak, muhammet yasin anadolu university - faculty of engineering - department of computerl engineering, Eskişehir, turkey , günal, serkan anadolu university - faculty of engineering - department of computerl engineering, Eskişehir, turkey
Title Of Article
THE IMPACT OF TEXT REPRESENTATION AND PREPROCESSING ON AUTHOR IDENTIFICATION
شماره ركورد
34316
Abstract
Author identification, one of the popular topics in text classification and natural language processing, basically aims to determine the author of a given text through various analyses. In the literature, different text representation approaches and use of preprocessing steps are considered for author identification problem. This paper aims to comprehensively examine the impact of text representation and preprocessing steps on author identification specifically for Turkish language. For this purpose, the contributions of all possible combinations of different text representation approaches, namely unigram and bigram, together with the preprocessing tasks, including stemming and stop-word removal, to the performance of author identification are investigated. For the experimental evaluation, a brand new dataset is constituted. Also, two different classification algorithms, namely Multinomial Naive Bayes and Sequential Minimal Optimization, are employed. The results of the experimental analysis reveal that using bigram features alone should be avoided. Besides, it is shown that stop-words should be kept inside the text while stemming can be preferred depending on the classification algorithm so that higher performance can be achieved for author identification.
From Page
218
NaturalLanguageKeyword
Author identification , Text classification , Text preprocessing , Text representation
JournalTitle
Anadolu University Journal of Science and Technology. A : Applied Sciences and Engineering
To Page
224
JournalTitle
Anadolu University Journal of Science and Technology. A : Applied Sciences and Engineering
Link To Document