Automatic Construction of Sememe Knowledge Bases via Dictionaries
This addresses the lack of SKBs in most languages, which are crucial for natural language processing, by providing an efficient alternative to time-consuming manual construction.
The authors tackled the problem of manually constructing sememe knowledge bases (SKBs) by proposing a fully automatic method using existing dictionaries, resulting in an English SKB that outperforms the manually built HowNet and both English and French SKBs improving performance in downstream tasks.
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 https://github.com/thunlp/DictSKB.