Aspect-based Query Expansion for Search Results Diversification

Query expansion is a prominent method to reformulate the under-specified and ambiguous query that helps to retrieve documents. This paper is aimed at diversifying web documents using a novel query expansion method. Our method leverage query suggestions and completions for pooling the terms which are used for final expansion. We then group similar candidates into the clusters. In this regards, a soft clattering algorithm is applied to the candidates, where each cluster represents different query aspect. The query is then expanded using the terms selected from cluster labels. Multiple lexical and semantic features are introduced to compute the relevancy between a query and the document. The lexical features use the textual contents of the query and document while, the semantic ones leverage the word-level semantics in word2vec model. Finally, a weighted linear ranking approach is applied to rank the documents retrieved for the query using the extracted features. The experimental results on Clueweb09 document collection using TREC 2012 Web Track queries clearly demonstrate that our proposed aspect-based query expansion method is effective to diversify the retrieved documents and outperformed baseline and some known related methods in terms of diversity metrics ERR-IA, α-nDCG and NRBP at the cut of 20.