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.
I Came, I Saw, I Explained: Benchmarking Multimodal LLMs on Figurative Meaning in Memes
MaiNLP, Center for Information and Language Processing, LMU Munich（MaiNLP，信息与语言处理中心，慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；National Research Council Canada（加拿大国家研究委员会）
Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Media Synthesis and Forensics Lab（媒体合成与取证实验室）
