Explainable and Sparse Representations of Academic Articles for Knowledge Exploration

Keng-Te Liao, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, PoChun Chen, Kuansan Wang, Shou-de Lin


Abstract
We focus on a recently deployed system built for summarizing academic articles by concept tagging. The system has shown great coverage and high accuracy of concept identification which could be contributed by the knowledge acquired from millions of publications. Provided with the interpretable concepts and knowledge encoded in a pre-trained neural model, we investigate whether the tagged concepts can be applied to a broader class of applications. We propose transforming the tagged concepts into sparse vectors as representations of academic documents. The effectiveness of the representations is analyzed theoretically by a proposed framework. We also empirically show that the representations can have advantages on academic topic discovery and paper recommendation. On these applications, we reveal that the knowledge encoded in the tagging system can be effectively utilized and can help infer additional features from data with limited information.
Anthology ID:
2020.coling-main.546
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6207–6216
Language:
URL:
https://aclanthology.org/2020.coling-main.546
DOI:
10.18653/v1/2020.coling-main.546
Bibkey:
Cite (ACL):
Keng-Te Liao, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, PoChun Chen, Kuansan Wang, and Shou-de Lin. 2020. Explainable and Sparse Representations of Academic Articles for Knowledge Exploration. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6207–6216, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Explainable and Sparse Representations of Academic Articles for Knowledge Exploration (Liao et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.546.pdf
Data
CoraMicrosoft Academic Graph