Neural network applications for automatic new topic identification

Abstract

Purpose -This study aims to propose an artificial neural network to identify automatically topic changes in a user session by using the statistical characteristics of queries, such as time intervals and query reformulation patterns. Design/methodology/approach -A sample data log from the Norwegian search engine FAST (currently owned by Overture) is selected to train the neural network and then the neural network is used to identify topic changes in the data log. Findings -A total of 98.4 percent of topic shifts and 86.6 percent of topic continuations were estimated correctly. Originality/value -Content analysis of search engine user queries is an important task, since successful exploitation of the content of queries can result in the design of efficient information retrieval algorithms for search engines, which can offer custom-tailored services to the web user. Identification of topic changes within a user search session is a key issue in the content analysis of search engine user queries.

Investigating the performance of automatic new topic identification across multiple datasets

“…Their approach was replicated on Excite search engine data (Ozmutlu and Cavdur, 2005a). Ozmutlu and Cavdur (2005b) and Ozmutlu, et al (2004a) proposed an artificial neural network to automatically identify topic changes, and showed that neural networks successfully provided new topic identification. Application of neural networks for automatic new topic identification does not contain semantic analysis, and relies on the statistical characteristics of the queries.…”

Empirical Analysis of the Rank Distribution of Relevant Documents in Web Search

“…A similar approach based on query content is reported in [9]. Here shifts between sessions are detected with the help of neural networks, also based on time stamp differences and search patterns.…”

Session Segmentation Method Based on Naïve Bayes Model

“…Multiple attributes session segmentation method. Documents [3,4,5,6] were written by same group of authors and gave the same definition for session. These papers adopt the same method to deal with query log and define that all the interactive information between the user and his search engine is a session.…”

“…We intend to group these activities together and refer them as a session. Previous documents [1,2,3,4,5,6,7,8] all utilize attribute time interval (TI) to achieve session segmentation. Other attributes like query likelihood (QL) and anchor likelihood (AL) are also used to divide session.…”

“…7 kinds of time intervals and 7 kinds of search patterns make up 49 combinations; any successive queries belong to one of the combinations. To make topic identification on such a data set, they have artificial neural network [4] , Multiple Linear Regression [5] and Monte Carlo simulation [6] . Their common feature is that they have a better performance in continuation identification than shift identification.…”