Abstract. 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.
REF proposes to disambiguate single-term queries by clustering and categorizing Web search results based on the meanings of WordNet for the queries.
Semantic-Based Grouping of Search Engine Results Using WordNet
APWeb/WAIM
Computer Science