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

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          Abstract

          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.

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          Author and article information

          Journal
          03 April 2021
          Article
          2104.01523
          d22bbb6f-92f5-45f9-958b-d1765c0f5b31

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          cs.CL

          Theoretical computer science
          Theoretical computer science

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