Controlled automatic query expansion based on a new method arisen in machine learning for detection of semantic relationships between terms

With the proliferation of textual data on the web, efficient access to relevant information to meet the user's needs has become an important problem in the information retrieval tasks. This problem is specially due to the short queries submitted usually by users to an information retrieval system to describe their needs. These systems have to complete the user needs with related terms in order to disambiguate the user query and better meet the user's needs. This paper presents a new method to define semantic relationships between terms of the relevant returned documents for a given query in order to improve the description of the user's needs, by expanding automatically the original query with related terms, and to improve the search results. Some experiments have been performed on the CLEF 2014 collection to show the effectiveness of our method.