Exploring Semantic Capacity of Terms

• URL: http://arxiv.org/abs/2010.01898v1
• Date: Mon, 5 Oct 2020 10:26:36 GMT
• Title: Exploring Semantic Capacity of Terms
• Authors: Jie Huang, Zilong Wang, Kevin Chen-Chuan Chang, Wen-mei Hwu, Jinjun Xiong
• Abstract summary: Understanding semantic capacity of terms will help many downstream tasks in natural language processing. 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.
• Score: 36.28318577160433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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