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incorporation from a space with a single dimension per term to a continuous vector space with a significantly
lower dimension (Diaz et al.,2016). The embedding models are normally trained in a broad corpus based on
term proximity. For instance, the goal of the Word2Vec model is to predict the next word(s), i.e., the context
window around the target word. This course aims to capture semantic and syntactic similarity between terms,
since similar words often share similar contexts. The primary objective of many IR approaches is to model
relevance (Saracevic,2016; Lavrenko & Croft,2017). In conclusion, the WE approaches seem to be more
promising way for AQE than the conventional approaches.

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Artificial Intelligence in the 4th Industrial Revolution