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Given the recent advances and progress in Natural Language Processing (NLP),extraction of semantic relationships has been at the top of the research agendain the last few years. This work has been mainly motivated by the fact thatbuilding knowledge graphs (KG) and bases (KB), as a key ingredient ofintelligent applications, is a never-ending challenge, since new knowledgeneeds to be harvested while old knowledge needs to be revised. Currently,approaches towards relation extraction from text are dominated by neural modelspracticing some sort of distant (weak) supervision in machine learning fromlarge corpora, with or without consulting external knowledge sources. In thispaper, we empirically study and explore the potential of a novel idea of usingclassical semantic spaces and models, e.g., Word Embedding, generated forextracting word association, in conjunction with relation extractionapproaches. The goal is to use these word association models to reinforcecurrent relation extraction approaches. We believe that this is a first attemptof this kind and the results of the study should shed some light on the extentto which these word association models can be used as well as the mostpromising types of relationships to be considered for extraction.

Exploring the Suitability of Semantic Spaces as Word Association Models for the Extraction of Semantic Relationships