Entity set expansion and synonym discovery are two critical NLP tasks.
Previous studies accomplish them separately, without exploring their
interdependencies. 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.

本文提出一种名为 SynSetExpan 的 NLP 框架，该框架能够同时进行实体集扩展和同义词发现两项任务，二者相互促进，通过众包打造了第一个大规模的同义词增强集扩展数据集 SE2，实验表明 SynSetExpan 在处理这两项任务时均具有效性。