Understanding Jargon: Combining Extraction and Generation for Definition Modeling

TL;DR

This paper introduces a combined extraction and generation approach to automatically produce high-quality definitions for jargon terms, significantly outperforming existing models.

Contribution

It presents a novel framework that integrates extraction and generation for jargon definition modeling, improving accuracy over prior methods.

Findings

Significantly higher BLEU scores (8.76 to 22.66)

Improved human-annotated scores (2.34 to 4.04)

Effective combination of extraction and generation techniques

Abstract

Can machines know what twin prime is? From the composition of this phrase, machines may guess twin prime is a certain kind of prime, but it is still difficult to deduce exactly what twin stands for without additional knowledge. Here, twin prime is a jargon - a specialized term used by experts in a particular field. Explaining jargon is challenging since it usually requires domain knowledge to understand. Recently, there is an increasing interest in extracting and generating definitions of words automatically. However, existing approaches, either extraction or generation, perform poorly on jargon. In this paper, we propose to combine extraction and generation for jargon definition modeling: first extract self- and correlative definitional information of target jargon from the Web and then generate the final definitions by incorporating the extracted definitional information. Our framework is remarkably simple but effective: experiments demonstrate our method can generate high-quality definitions for jargon and outperform state-of-the-art models significantly, e.g., BLEU score from 8.76 to 22.66 and human-annotated score from 2.34 to 4.04.