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Semantic relationships, such as hyponym-hypernym, cause-effect,meronym-holonym etc. between a pair of entities in a sentence are usuallyreflected through syntactic patterns. Automatic extraction of such patternsbenefits several downstream tasks, including, entity extraction, ontologybuilding, and question answering. Unfortunately, automatic extraction of suchpatterns has not yet received much attention from NLP and information retrievalresearchers. In this work, we propose an attention-based supervised deeplearning model, ASPER, which extracts syntactic patterns between entitiesexhibiting a given semantic relation in the sentential context. We validate theperformance of ASPER on three distinct semantic relations -- hyponym-hypernym,cause-effect, and meronym-holonym on six datasets. Experimental results showthat for all these semantic relations, ASPER can automatically identify acollection of syntactic patterns reflecting the existence of such a relationbetween a pair of entities in a sentence. In comparison to the existingmethodologies of syntactic pattern extraction, ASPER's performance issubstantially superior.

ASPER: Attention-based Approach to Extract Syntactic Patterns denoting Semantic Relations in Sentential Context