  Semantic relationships, such as hyponym-hypernym, cause-effect,
meronym-holonym etc. between a pair of entities in a sentence are usually
reflected through syntactic patterns. Automatic extraction of such patterns
benefits several downstream tasks, including, entity extraction, ontology
building, and question answering. Unfortunately, automatic extraction of such
patterns has not yet received much attention from NLP and information retrieval
researchers. In this work, we propose an attention-based supervised deep
learning model, ASPER, which extracts syntactic patterns between entities
exhibiting a given semantic relation in the sentential context. We validate the
performance of ASPER on three distinct semantic relations -- hyponym-hypernym,
cause-effect, and meronym-holonym on six datasets. Experimental results show
that for all these semantic relations, ASPER can automatically identify a
collection of syntactic patterns reflecting the existence of such a relation
between a pair of entities in a sentence. In comparison to the existing
methodologies of syntactic pattern extraction, ASPER's performance is
substantially superior.
