Information Processing and Management 54 (2018) 1–13 Contents lists available at ScienceDirect Information Processing and Management journal homepage: www.elsevier.com/locate/infoproman A Prospect-Guided global query expansion strategy using word embeddings Francis C. Fernández-Reyes a , Jorge Hermosillo-Valadez a,* , Manuel Montes-y-Gómez b a Centro de Investigación en Ciencias-(IICBA), Universidad Autónoma del Estado de Morelos, Av. Universidad 1001, Cuernavaca, Morelos 62209, Mexico b Instituto Nacional de Astrofísica, Óptica y Electrónica, Santa María Tonantzintla, Puebla 72840, Mexico a r t i c l e i n f o Article history: Received 10 February 2017 Revised 27 June 2017 Accepted 9 September 2017 Keywords: Global query expansion Word embeddings Information retrieval Candidate terms pooling methods a b s t r a c t The effectiveness of query expansion methods depends essentially on identifying good can- didates, or prospects, semantically related to query terms. Word embeddings have been used recently in an attempt to address this problem. Nevertheless query disambiguation is still necessary as the semantic relatedness of each word in the corpus is modeled, but choosing the right terms for expansion from the standpoint of the un-modeled query se- mantics remains an open issue. In this paper we propose a novel query expansion method using word embeddings that models the global query semantics from the standpoint of prospect vocabulary terms. The proposed method allows to explore query-vocabulary se- mantic closeness in such a way that new terms, semantically related to more relevant topics, are elicited and added in function of the query as a whole. The method includes candidates pooling strategies that address disambiguation issues without using exogenous resources. We tested our method with three topic sets over CLEF corpora and compared it across different Information Retrieval models and against another expansion technique using word embeddings as well. Our experiments indicate that our method achieves sig- nificant results that outperform the baselines, improving both recall and precision metrics without relevance feedback. © 2017 Elsevier Ltd. All rights reserved. 1. Introduction Over the years, query expansion (QE) methods have been proposed as an effective way to address the query-document vocabulary mismatch problem in Information Retrieval (IR) tasks (Vechtomova, 2009; White & Horvitz, 2015). The aim is to enrich the query by adding semantically related words, mainly using synonyms. Approaches to QE can be classified into global or local methods. On the one hand, global methods expand the original query independently of any retrieval result. Typically, WordNet is the standard exogenous tool of choice for selecting new terms semantically associated to the original ones (Pal, Mitra, & Datta, 2014). On the other hand, local methods use relevance feedback, whereby they perform a first retrieval whose outcome is actually used for selecting the most promising terms * Corresponding author. E-mail addresses: fcaridad@uaem.mx (F.C. Fernández-Reyes), jhermosillo@uaem.mx (J. Hermosillo-Valadez), mmontesg@inaoep.mx (M. Montes-y- Gómez). http://dx.doi.org/10.1016/j.ipm.2017.09.001 0306-4573/© 2017 Elsevier Ltd. All rights reserved.