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Title: ASPER: Attention-based approach to extract syntactic patterns denoting semantic relations in sentential context
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.  more » « less
Award ID(s):
1909916
PAR ID:
10495054
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Elsevier
Date Published:
Journal Name:
Natural Language Processing Journal
Volume:
3
Issue:
C
ISSN:
2949-7191
Page Range / eLocation ID:
100011
Subject(s) / Keyword(s):
Syntactic pattern, Syntactic pattern extraction
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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