A sememe is defined as the minimum semantic unit in linguistics. Sememe
knowledge bases (SKBs), which comprise words annotated with sememes, enable
sememes to be applied to natural language processing. So far a large body of
research has showcased the unique advantages and effectiveness of SKBs in
various tasks. However, most languages have no SKBs, and manual construction of
SKBs is time-consuming and labor-intensive. To tackle this challenge, we
propose a simple and fully automatic method of building an SKB via an existing
dictionary. We use this method to build an English SKB and a French SKB, and
conduct comprehensive evaluations from both intrinsic and extrinsic
perspectives. Experimental results demonstrate that the automatically built
English SKB is even superior to HowNet, the most widely used SKB that takes
decades to build manually. And both the English and French SKBs can bring
obvious performance enhancement in multiple downstream tasks. All the code and
data of this paper (except the copyrighted dictionaries) can be obtained at
this https URL

本文提出了一种基于现有词典完全自动地构建英文和法语语义知识库 (SKBs) 的简单方法，并进行了综合评估。实验结果表明，自动构建的英语 SKB 甚至优于需要数十年手动构建的 HowNet，在多个后续任务中，英语和法语 SKBs 都能带来明显的性能提升。