Definition modeling: literature review and dataset analysis

Abstract

<abstract><p>Definition modeling, the task of generating a definition for a given term, is a relatively new area of research applied in evaluating word embeddings. Automatic generation of dictionary quality definitions has many applications in natural language processing, such as sentiment analysis, machine translation, and word sense disambiguation. Additionally, definition modeling is also helpful for evaluating the quality of word embeddings. As more research is done in this field, the need for a summary of different applications, approaches, and obstacles grows apparent. This review provides an overview of the current research in definition modeling and a list of future directions and trends.</p></abstract>

VER: Unifying Verbalizing Entities and Relations

“…The answer is "Yes". Recent studies show that by giving the surface name of an entity (and its context), models (after training) can generate coherent sentences to represent it, i.e., definition modeling (Noraset et al, 2017;Gadetsky et al, 2018;Bevilacqua et al, 2020;August et al, 2022;Huang et al, 2022b;Gardner et al, 2022), and by giving the surface names of a pair of entities, machines can generate coherent sentences describing their relationships, i.e., (open) relation modeling (Huang et al, 2022a,c). However, verbalizing entities requires understanding relationships between entities, and verbalizing entity relationships requires understanding entities themselves, while existing works deal with entity and relation verbalization separately, ignoring the connections between them.…”