# Mining Entity Synonyms with Efficient Neural Set Generation

arXiv: https://arxiv.org/abs/1811.07032 · HTML: https://mlsanity.com/paper/1811.07032/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2018.

## Built on by (most cited first, 2 of 2)

- [KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph](https://mlsanity.com/paper/2103.08893/) (2021)
- [Training ELECTRA Augmented with Multi-word Selection](https://mlsanity.com/paper/2106.00139/) (2021)

## Abstract (arXiv)

Mining entity synonym sets (i.e., sets of terms referring to the same entity) is an important task for many entity-leveraging applications. Previous work either rank terms based on their similarity to a given query term, or treats the problem as a two-phase task (i.e., detecting synonymy pairs, followed by organizing these pairs into synonym sets). However, these approaches fail to model the holistic semantics of a set and suffer from the error propagation issue. Here we propose a new framework, named SynSetMine, that efficiently generates entity synonym sets from a given vocabulary, using example sets from external knowledge bases as distant supervision. SynSetMine consists of two novel modules: (1) a set-instance classifier that jointly learns how to represent a permutation invariant synonym set and whether to include a new instance (i.e., a term) into the set, and (2) a set generation algorithm that enumerates the vocabulary only once and applies the learned set-instance classifier to detect all entity synonym sets in it. Experiments on three real datasets from different domains demonstrate both effectiveness and efficiency of SynSetMine for mining entity synonym sets.