A survey of Web clustering engines

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Comment-based multi-view clustering of web 2.0 items (2014, methodology): “Clustering has been an effective method to address this information overload, helping in several different contexts: in automatically organizing web resources for content providers, and in diver-sifying search results in web document ranking [8].”
Search Result Clustering in Collaborative Sound Collections (2020, background): “Clustering engines can complement the search by providing a faster way to retrieve relevant items, facilitating topic exploration and preventing the overlook of information [6].”
Spoken Content Retrieval—Beyond Cascading Speech Recognition with Text Retrieval (2015, background): “2) Query-based Local Semantic Structuring: There were extensive work on local semantic structuring for both text [348] and spoken content [349], [350].”
A Survey of Automatic Query Expansion in Information Retrieval (2012, background): “A recent review of this relatively large body of literature is given in [Carpineto et al. 2009].”
Fast and Accurate Annotation of Short Texts with Wikipedia Pages (2010, background): “[2]), is based only on syntactic and statistical features and thus it could benefit from Tagme’s annotation to improve the effectiveness of the labeling and the clustering phases.”