Leveraging Capsule Routing to Associate Knowledge with Medical Literature Hierarchically

Xin Liu, Qingcai Chen, Junying Chen, Wenxiu Zhou, Tingyu Liu, Xinlan Yang, Weihua Peng


Abstract
Integrating knowledge into text is a promising way to enrich text representation, especially in the medical field. However, undifferentiated knowledge not only confuses the text representation but also imports unexpected noises. In this paper, to alleviate this problem, we propose leveraging capsule routing to associate knowledge with medical literature hierarchically (called HiCapsRKL). Firstly, HiCapsRKL extracts two empirically designed text fragments from medical literature and encodes them into fragment representations respectively. Secondly, the capsule routing algorithm is applied to two fragment representations. Through the capsule computing and dynamic routing, each representation is processed into a new representation (denoted as caps-representation), and we integrate the caps-representations as information gain to associate knowledge with medical literature hierarchically. Finally, HiCapsRKL are validated on relevance prediction and medical literature retrieval test sets. The experimental results and analyses show that HiCapsRKLcan more accurately associate knowledge with medical literature than mainstream methods. In summary, HiCapsRKL can efficiently help selecting the most relevant knowledge to the medical literature, which may be an alternative attempt to improve knowledge-based text representation. Source code is released on GitHub.
Anthology ID:
2021.emnlp-main.285
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3518–3532
Language:
URL:
https://aclanthology.org/2021.emnlp-main.285
DOI:
10.18653/v1/2021.emnlp-main.285
Bibkey:
Cite (ACL):
Xin Liu, Qingcai Chen, Junying Chen, Wenxiu Zhou, Tingyu Liu, Xinlan Yang, and Weihua Peng. 2021. Leveraging Capsule Routing to Associate Knowledge with Medical Literature Hierarchically. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3518–3532, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Leveraging Capsule Routing to Associate Knowledge with Medical Literature Hierarchically (Liu et al., EMNLP 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.emnlp-main.285.pdf
Video:
 https://preview.aclanthology.org/ingestion-script-update/2021.emnlp-main.285.mp4
Code
 gdls/hicapsrkl