Context-aware query design combines knowledge and data for efficient reading and reasoning

Emilee Holtzapple, Brent Cochran, Natasa Miskov-Zivanov


Abstract
The amount of biomedical literature has vastly increased over the past few decades. As a result, the sheer quantity of accessible information is overwhelming, and complicates manual information retrieval. Automated methods seek to speed up information retrieval from biomedical literature. However, such automated methods are still too time-intensive to survey all existing biomedical literature. We present a methodology for automatically generating literature queries that select relevant papers based on biological data. By using differentially expressed genes to inform our literature searches, we focus information extraction on mechanistic signaling details that are crucial for the disease or context of interest.
Anthology ID:
2021.bionlp-1.26
Volume:
Proceedings of the 20th Workshop on Biomedical Language Processing
Month:
June
Year:
2021
Address:
Online
Venues:
BioNLP | NAACL
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
238–246
Language:
URL:
https://aclanthology.org/2021.bionlp-1.26
DOI:
10.18653/v1/2021.bionlp-1.26
Bibkey:
Cite (ACL):
Emilee Holtzapple, Brent Cochran, and Natasa Miskov-Zivanov. 2021. Context-aware query design combines knowledge and data for efficient reading and reasoning. In Proceedings of the 20th Workshop on Biomedical Language Processing, pages 238–246, Online. Association for Computational Linguistics.
Cite (Informal):
Context-aware query design combines knowledge and data for efficient reading and reasoning (Holtzapple et al., BioNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.bionlp-1.26.pdf