@inproceedings{xu-etal-2023-exploring,
title = "Exploring and Verbalizing Academic Ideas by Concept Co-occurrence",
author = "Xu, Yi and
Sheng, Shuqian and
Xue, Bo and
Fu, Luoyi and
Wang, Xinbing and
Zhou, Chenghu",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.727",
doi = "10.18653/v1/2023.acl-long.727",
pages = "13001--13027",
abstract = "Researchers usually come up with new ideas only after thoroughly comprehending vast quantities of literature. The difficulty of this procedure is exacerbated by the fact that the number of academic publications is growing exponentially. In this study, we devise a framework based on concept co-occurrence for academic idea inspiration, which has been integrated into a research assistant system. From our perspective, the emergence of a new idea can be regarded as the fusion of two concepts that co-occur in an academic paper. We construct evolving concept graphs according to the co-occurrence relationship of concepts from 20 disciplines or topics. Then we design a temporal link prediction method based on masked language model to explore potential connections between different concepts. To verbalize the newly discovered connections, we also utilize the pretrained language model to generate a description of an idea based on a new data structure called co-occurrence citation quintuple. We evaluate our proposed system using both automatic metrics and human assessment. The results demonstrate that our system has broad prospects and can assist researchers in expediting the process of discovering new ideas.",
}
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<abstract>Researchers usually come up with new ideas only after thoroughly comprehending vast quantities of literature. The difficulty of this procedure is exacerbated by the fact that the number of academic publications is growing exponentially. In this study, we devise a framework based on concept co-occurrence for academic idea inspiration, which has been integrated into a research assistant system. From our perspective, the emergence of a new idea can be regarded as the fusion of two concepts that co-occur in an academic paper. We construct evolving concept graphs according to the co-occurrence relationship of concepts from 20 disciplines or topics. Then we design a temporal link prediction method based on masked language model to explore potential connections between different concepts. To verbalize the newly discovered connections, we also utilize the pretrained language model to generate a description of an idea based on a new data structure called co-occurrence citation quintuple. We evaluate our proposed system using both automatic metrics and human assessment. The results demonstrate that our system has broad prospects and can assist researchers in expediting the process of discovering new ideas.</abstract>
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%0 Conference Proceedings
%T Exploring and Verbalizing Academic Ideas by Concept Co-occurrence
%A Xu, Yi
%A Sheng, Shuqian
%A Xue, Bo
%A Fu, Luoyi
%A Wang, Xinbing
%A Zhou, Chenghu
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F xu-etal-2023-exploring
%X Researchers usually come up with new ideas only after thoroughly comprehending vast quantities of literature. The difficulty of this procedure is exacerbated by the fact that the number of academic publications is growing exponentially. In this study, we devise a framework based on concept co-occurrence for academic idea inspiration, which has been integrated into a research assistant system. From our perspective, the emergence of a new idea can be regarded as the fusion of two concepts that co-occur in an academic paper. We construct evolving concept graphs according to the co-occurrence relationship of concepts from 20 disciplines or topics. Then we design a temporal link prediction method based on masked language model to explore potential connections between different concepts. To verbalize the newly discovered connections, we also utilize the pretrained language model to generate a description of an idea based on a new data structure called co-occurrence citation quintuple. We evaluate our proposed system using both automatic metrics and human assessment. The results demonstrate that our system has broad prospects and can assist researchers in expediting the process of discovering new ideas.
%R 10.18653/v1/2023.acl-long.727
%U https://aclanthology.org/2023.acl-long.727
%U https://doi.org/10.18653/v1/2023.acl-long.727
%P 13001-13027
Markdown (Informal)
[Exploring and Verbalizing Academic Ideas by Concept Co-occurrence](https://aclanthology.org/2023.acl-long.727) (Xu et al., ACL 2023)
ACL
- Yi Xu, Shuqian Sheng, Bo Xue, Luoyi Fu, Xinbing Wang, and Chenghu Zhou. 2023. Exploring and Verbalizing Academic Ideas by Concept Co-occurrence. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13001–13027, Toronto, Canada. Association for Computational Linguistics.