We introduce and study semantic capacity of terms. For example, the semantic capacity of artificial intelligence is higher than that of linear regression since artificial intelligence possesses a broader meaning scope. Understanding semantic capacity of terms will help many downstream tasks in natural language processing. For this purpose, we propose a two-step model to investigate semantic capacity of terms, which takes a large text corpus as input and can evaluate semantic capacity of terms if the text corpus can provide enough co-occurrence information of terms. Extensive experiments in three fields demonstrate the effectiveness and rationality of our model compared with well-designed baselines and human-level evaluations.
@article{arxiv.2010.01898,
title = {Exploring Semantic Capacity of Terms},
author = {Jie Huang and Zilong Wang and Kevin Chen-Chuan Chang and Wen-mei Hwu and Jinjun Xiong},
journal= {arXiv preprint arXiv:2010.01898},
year = {2020}
}