Open AccessUsing support vector machines for automatic new topic identification
Author(s) -
Ozmutlu Seda,
Ozmutlu H. Cenk,
Spink Amanda
Publication year - 2007
Publication title -
proceedings of the american society for information science and technology
Language(s) - English
Resource type - Journals
eISSN - 1550-8390
pISSN - 0044-7870
DOI - 10.1002/meet.145044028
Subject(s) - support vector machine , identification (biology) , computer science , machine learning , artificial neural network , artificial intelligence , data mining , sample (material) , norwegian , search engine , information retrieval , linguistics , chemistry , botany , philosophy , chromatography , biology
Recent studies on automatic new topic identification in Web search engine user sessions demonstrated that learning algorithms such as neural networks and regression have been fairly successful in automatic new topic identification. In this study, we investigate whether another learning algorithm, Support Vector Machines (SVM) are successful in terms of identifying topic shifts and continuations. Sample data logs from the Norwegian search engine FAST (currently owned by Overture) and Excite are used in this study. Findings of this study suggest that support vector machines' performance depends on the characteristics of the dataset it is applied on.