Terms used in search queries often have multiple meanings. Consequently, search results corresponding to different meanings may be retrieved, making identifying relevant results inconvenient and time-consuming. In this paper, we propose a new solution to address this issue. Our method groups the search results based on the different meanings of the query. It utilizes the semantic dictionary WordNet to determine the basic meanings or senses of each query term and similar senses are merged to improve grouping quality. Our grouping algorithm employs a combination of categorization and clustering techniques. Our experimental results indicate that our method can achieve high grouping accuracy.
Abstract. In this paper, we propose a method for identifying and ranking possible categories of any user query based on the meanings and common usages of the terms and phrases within the query. Our solution utilizes WordNet and Wikipedia to recognize phrases and to determine the basic meanings and usages of each term or phrase in a query. The categories are ranked based on their likelihood in capturing the query's intention. Experimental results show that our method can achieve high accuracy.
ABSTRACT. Current search engines do not explicitly take different meanings and usages of user queries into consideration when they rank the search results. As a result, they tend to retrieve results that cover the most popular meanings or usages of the query. Consequently, users who want results that cover a rare meaning or usage of query or results that cover all different meanings/usages may have to go through a large number of results in order to find the desired ones. Another problem with current search engines is that they do not adequately take users' intention into consideration. In this paper, we introduce a novel result ranking algorithm (mNIR) that explicitly takes result novelty, user intention-based distribution and result relevancy into consideration and mixes them to achieve better result ranking. We analyze how giving different emphasis to the above three aspects would impact the overall ranking of the results. Our approach builds on our previous method for identifying and ranking possible categories of any user query based on the meanings and usages of the terms and phrases within the query. These categories are also used to generate category queries for retrieving results matching different meanings/usages of the original user query. Our experimental results show that the proposed algorithm can outperform state-of-the-art diversification approaches.
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