Set-aware Entity Synonym Discovery with Flexible Receptive Fields

Abstract

Entity synonym discovery (ESD) from text corpus is an essential problem in many entity-leveraging applications, e.g., web search and question answering. This paper aims to address three limitations that widely exist in the current ESD solutions: 1) the lack of effective utilization for synonym set information; 2) the feature extraction of entities from restricted receptive fields; and 3) the incapacity to capture higher-order contextual information. We propose a novel set-aware ESD model that enables a flexible receptive field for ESD by making a breakthrough in using entity synonym set information. The contextual information of entities and entity synonym sets are arranged by a two-level network from which entities and entity synonym sets can be mapped into the same embedding space to facilitate ESD by encoding the high-order contexts from flexible receptive fields. Extensive experimental results on public datasets show that our model consistently outperforms the state-of-the-art with significant improvement.

A Visually Enhanced Neural Encoder for Synset Induction

“…In other words, only a collection of tags is given for clustering identical semantics. To this end, these methods can roughly be grouped into two categories: corpus statistics and pattern-based methods [7][8][9][10][11] and distributional representation-based methods [13][14][15][16].…”

“…Given a collection of tags, the synset induction algorithms aim to cluster the tags such that each cluster refers to identical semantics. These methods can roughly be grouped into two categories: corpus statistics and patterns based [7][8][9][10][11][12] and distributional representation based [13][14][15][16]. Those methods generally achieve a promising performance.…”

A Hybrid Approach to Discover Entity Synonyms

“…Chenwei Zhang et al(2020) proposed a neural network-based model named SYNONYMNET to check whether two entities are synonyms of each other or not by using different contexts. The experimental results show an improvement in mean average precision when compared with existing baseline models.…”