@inproceedings{huang-etal-2020-exploring,
title = "Exploring Semantic Capacity of Terms",
author = "Huang, Jie and
Wang, Zilong and
Chang, Kevin and
Hwu, Wen-mei and
Xiong, JinJun",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.684",
doi = "10.18653/v1/2020.emnlp-main.684",
pages = "8509--8518",
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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<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.</abstract>
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%0 Conference Proceedings
%T Exploring Semantic Capacity of Terms
%A Huang, Jie
%A Wang, Zilong
%A Chang, Kevin
%A Hwu, Wen-mei
%A Xiong, JinJun
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F huang-etal-2020-exploring
%X 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.
%R 10.18653/v1/2020.emnlp-main.684
%U https://aclanthology.org/2020.emnlp-main.684
%U https://doi.org/10.18653/v1/2020.emnlp-main.684
%P 8509-8518
Markdown (Informal)
[Exploring Semantic Capacity of Terms](https://aclanthology.org/2020.emnlp-main.684) (Huang et al., EMNLP 2020)
ACL
- Jie Huang, Zilong Wang, Kevin Chang, Wen-mei Hwu, and JinJun Xiong. 2020. Exploring Semantic Capacity of Terms. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8509–8518, Online. Association for Computational Linguistics.