Evaluation of Automatically Constructed Word Meaning Explanations

TL;DR

This paper introduces an automated tool for generating word meaning explanations from large corpora, reducing reliance on expert lexicographers, with a focus on nouns and evaluation in Czech and English.

Contribution

The paper presents a novel automatic method for constructing word explanations using corpus data, offering a scalable alternative to manual dictionary writing.

Findings

Approximately 90% of explanations contain useful data for understanding meanings.

The approach is partly language-independent, demonstrated in Czech and English.

Post-editing is often needed to refine the generated explanations.

Abstract

Preparing exact and comprehensive word meaning explanations is one of the key steps in the process of monolingual dictionary writing. In standard methodology, the explanations need an expert lexicographer who spends a substantial amount of time checking the consistency between the descriptive text and corpus evidence. In the following text, we present a new tool that derives explanations automatically based on collective information from very large corpora, particularly on word sketches. We also propose a quantitative evaluation of the constructed explanations, concentrating on explanations of nouns. The methodology is to a certain extent language independent; however, the presented verification is limited to Czech and English. We show that the presented approach allows to create explanations that contain data useful for understanding the word meaning in approximately 90% of cases. However, in many cases, the result requires post-editing to remove redundant information.