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sense discovery problem. Given a query and a list of result pages, our unsupervised method detects word sense communities in the extracted keyword network. The documents are assigned to several refined word sense communities to form clusters. We use the modularity score of the discovered keyword community structure to
With the increasing number of Web documents in the Internet, the most popular keyword-matching-based search engines, such as Google, often return a long list of search results ranked based on their relevance and importance to the query. To cluster the search engine results can help users find the results in several
In our earlier works on VAST (visuAl & semantic image search) system, the semantic network effectively associated keywords and visual feature clusters. However, we only concerned about the construction of the semantic network before, and did not consider the updating of the semantic network. In this paper, an
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