---
title: "ConDef Abstract"
type: concept
source: https://www.jemoka.com/posts/kbhcondef_abstract/
confidence: high
status: active
---

Current automated lexicography (term definition) techniques cannot include contextual or new term information as a part of its synthesis. We propose a novel data harvesting scheme leveraging lead paragraphs in Wikipedia to train automated context-aware lexicographical models. Furthermore, we present ConDef, a fine-tuned BART trained on the harvested data that defines vocabulary terms from a short context. ConDef is determined to be highly accurate in context-dependent lexicography as validated on ROUGE-1 and ROUGE-L measures in an 1000-item withheld test set, achieving scores of 46.40% and 43.26% respectively. Furthermore, we demonstrate that ConDef&rsquo;s synthesis serve as good proxies for term definitions by achieving ROUGE-1 measure of 27.79% directly against gold-standard WordNet definitions.Accepted to the 2022 SAI Computing Conference, to be published on Springer Nature&rsquo;s Lecture Notes on Networks and Systems Current automated lexicography (term definition) techniques cannot include contextual or new term information as a part of its synthesis. We propose a novel data harvesting scheme leveraging lead paragraphs in Wikipedia to train automated context-aware lexicographical models. Furthermore, we present ConDef, a fine-tuned BART trained on the harvested data that defines vocabulary terms from a short context. ConDef is determined to be highly accurate in context-dependent lexicography as validated on ROUGE-1 and ROUGE-L measures in an 1000-item withheld test set, achieving scores of 46.40% and 43.26% respectively. Furthermore, we demonstrate that ConDef&rsquo;s synthesis serve as good proxies for term definitions by achieving ROUGE-1 measure of 27.79% directly against gold-standard WordNet definitions.
