Automatic Construction of Sememe Knowledge Bases via Dictionaries

TL;DR

This paper presents a fully automatic method to construct sememe knowledge bases from dictionaries, successfully building English and French SKBs that outperform manual ones and improve downstream NLP tasks.

Contribution

The paper introduces a novel automatic approach to build sememe knowledge bases from dictionaries, reducing manual effort and demonstrating superior quality and utility.

Findings

Automatically built English SKB surpasses HowNet in quality.

Both English and French SKBs enhance downstream NLP tasks.

The method is effective for multiple languages and tasks.

Abstract

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.