Categorizing Search Results Using WordNet and Wikipedia

Hemayati, Reza Taghizadeh; Meng, Weiyi; Yu, Clement

doi:10.1007/978-3-642-32281-5_18

Cited by 2 publications

(2 citation statements)

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“…If only the original query Q is used to retrieve results, then to ensure results for some rare meanings/usages are obtained, we often need to retrieve hundreds or even thousands of SRRs. To overcome this problem we use the label query generated for each category [19] to retrieve relevant SRRs. But if only the label queries are used, we may miss some important relevant SRRs with respect to the original query.…”

Section: Selection Methods (Sm)mentioning

confidence: 99%

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mNIR: Diversifying Search Results Based on a Mixture of Novelty, Intention and Relevance

Hemayati

Dehkordi

Meng

2012

Web Information Systems Engineering - WISE 2012

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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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Section: Selection Methods (Sm)mentioning

confidence: 99%

“…First, we leverage the previous works [9,19] to find all possible intentions (meanings/usages) of a query based on Wikipedia and WordNet. This method also estimates the importance of each meaning/usage.…”

Section: Introductionmentioning

confidence: 99%