  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.
