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arXiv · 2010.01898

Exploring Semantic Capacity of Terms

Jie Huang·Zilong Wang·Kevin Chen-Chuan Chang·Wen-mei Hwu·Jinjun Xiong
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Abstract

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.

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Jie Huang, Zilong Wang, Kevin Chen-Chuan Chang, Wen-mei Hwu, Jinjun Xiong. 2020-10-05. Exploring Semantic Capacity of Terms. https://arxiv.org/abs/2010.01898

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