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Title: SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery
Entity set expansion and synonym discovery are two critical NLP tasks. Previous studies accomplish them separately, without exploring their interdependences. In this work, we hypothesize that these two tasks are tightly coupled because two synonymous entities tend to have similar likelihoods of belonging to various semantic classes. This motivates us to design SynSetExpan, a novel framework that enables two tasks to mutually enhance each other. SynSetExpan uses a synonym discovery model to include popular entities’ infrequent synonyms into the set, which boosts the set expansion recall. Meanwhile, the set expansion model, being able to determine whether an entity belongs to a semantic class, can generate pseudo training data to fine-tune the synonym discovery model towards better accuracy. To facilitate the research on studying the interplays of these two tasks, we create the first large-scale Synonym-Enhanced Set Expansion (SE2) dataset via crowdsourcing. Extensive experiments on the SE2 dataset and previous benchmarks demonstrate the effectiveness of SynSetExpan for both entity set expansion and synonym discovery tasks.  more » « less
Award ID(s):
1956151 1741317 1704532
NSF-PAR ID:
10279816
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
EMNLP'20: 2020 Conf. on Empirical Methods in Natural Language Processing, Nov. 2020
Volume:
2020
Issue:
1
Page Range / eLocation ID:
8292 to 8307
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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