Improving Identification of Latent User Goals through Search-Result Snippet Classification

• Published in: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence pp. 683 - 686
• Main Authors: He, Kuan-Yu, Chang, Yao-Sheng, Lu, Wen-Hsiang
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 02.11.2007
• Series: ACM Conferences
• Subjects: Computing methodologies > Machine learning; Computing methodologies > Machine learning > Learning paradigms > Supervised learning > Supervised learning by classification; Computing methodologies > Machine learning > Machine learning approaches > Classification and regression trees; Information systems > Information retrieval; Information systems > Information retrieval > Evaluation of retrieval results
• ISBN: 9780769530260; 0769530265
• DOI: 10.1109/WI.2007.137

In this paper, we propose an enhanced approach to improving our previous method which employs syntactic structures (verb-object pairs) to identify latent user goals. Our new approach employs a supervised-learning method to learn hint verbs and considers URL information and title information to classify snippets into three coarse categories, which are resource-seeking, informational, and navigational. Also, we propose three different models to identify three different categories of specific latent user goals from the classified snippets.