Evaluation of Automatically Constructed Word Meaning Explanations
This addresses the time-consuming task for lexicographers in dictionary writing, but it is incremental as it builds on existing corpus-based methods with a focus on Czech and English.
The paper tackles the problem of automating word meaning explanations for monolingual dictionaries by presenting a tool that uses large corpora and word sketches, achieving useful explanations in approximately 90% of cases, though often requiring post-editing to remove redundancy.
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.