Abstract
Automatic generation of entity synonyms plays a pivotal role in various natural language processing applications, such as search engines, question-answering systems, and taxonomy construction. Previous research on generating entity synonym sets has typically relied on approaches that involve sorting and pruning candidate entities or solving the problem in a two-stage manner (i.e., initially identifying pairs of synonyms and subsequently aggregating them into sets). Nevertheless, these approaches tend to disregard global entity information and are susceptible to error propagation issues. This paper introduces an innovative approach to generating entity synonym sets that leverages a flexible perception mechanism and multi-layer contextual information. Firstly, to determine whether to incorporate new candidate entities into synonym sets, the approach integrates a neural network classifier with a flexible perceptual field. Within the classifier, the approach builds a three-layer interactive network, and connects the entity layer, set layer, and sentence layer to the same embedding space to extract synonym features. Secondly, we introduce a dynamic-weight-based algorithm for synthesizing entity synonym sets, leveraging a neural network classifier trained to generate entity synonym sets from the candidate entity vocabulary. Finally, extensive experimental results on three public datasets demonstrate that our approach outperforms other comparable approaches in generating entity synonym sets.