Author Details

Alias name is the surnames for a known name. Extracting and validating alias names is an interesting research topic in language processing and has a number of Natural language processing applications like Information extraction, Information retrieval, Sentimental analysis, Question and answering. Alias name validation involves the process of validating whether a name is alias name or not. In this work, seven statistical similarity coefficients were used as features in classifier to validate alias names. For each name-alias pair, seven statistical similarity coefficient values were calculated and used as features to train a classifier. The trained classifier is then employed to classify whether a name-alias pair is valid or not. Experiments were conducted using Indian name-alias data that has data for 15 persons containing 35 name-alias pairs. Results show that SVM classifier with Radial Basis Function Kernel outperforms all the other classifiers in terms of overall accuracy.

Keywords

Alias Name Extraction, Information Extraction, Web Mining.

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Validating the Performance of Personalization Techniques in Search Engine

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Authors

A. Suruliandi ¹, T. Dhiliphan Rajkumar ¹, P. Selvaperumal ¹

Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN

Source

ICTACT Journal on Soft Computing, Vol 5, No 3 (2015), Pagination: 965-970

Abstract

User profiling is an important and basic component in personalized search engine. Search engines respond to a user's query by using the bag-of-words model, which matches keyword between the query and web documents but ignore contexts and users' preferences. Personalized search greatly improves the search results as of the results provided by the search engine without personalization. In this paper, the performance of personalized search based on content analysis and personalized search based on user group have been evaluated. In personalized search based on content analysis the contents are traced by finding the user's browsed documents and search history, which reduce the users search time. In user profile only user preference alone is taken into consideration. The experimental results show that the personalized search based on user group method having higher precision and recall rate than the content analysis method.

Keywords

Search Engine, Personalization, User Profile, Content Analysis.

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