A Survey on Computational Intelligence Applications in Information Retrieval

Abstract

Computational Intelligence (CI) techniques have been widely employed in information retrieval (IR). CI approaches include everything from evolutionary computation and metaheuristics to machine learning techniques. These methods may be classified based on the problem domain for which they are employed in conjunction with the IR dataset representation formats. When utilizing these methods, the Vector Term Model (VTM) and Vector Feature Model (VFM) are the data representation methods that were used. The VTM represents the document and the query as vectors of term-weights. CI approaches are commonly used in VTM in conjunction with the classical Vector Space Model (VSM), while Term-weights are substituted by feature-weights in the VFM to yield feature vectors of query-document pairs. These features represent the query-document similarity matching values (such as cosine similarity and BM25), the query-document term-weights (such as TF-IDF, Okapi, and Language Models), and the document's online reputation. This paper reviews some of the research works on applying CI approaches to the VTM and VFM.