An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Leveraging context in user-centric entity detection systems
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
PLEDS: A Personalized Entity Detection System Based on Web Log Mining Techniques
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
Extended gloss overlaps as a measure of semantic relatedness
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Given the proliferation of technology sites and the growing diversity of their readership, readers are more and more likely to encounter specialized language and terminology that they may lack the sufficient background to understand. Such sites may lose readership and the experience of readers may be impacted negatively if readers cannot quickly and easily find information about terms they wish to learn more about. We developed a system using a fusion of web log mining techniques that extracts, identifies, and recommends personalized terms to readers by utilizing information found in individual and global web query logs. In addition, the system presents relevant information related to these terms inline with the text. Our system outperforms some other related systems developed in the literature with special regard to usability.