Paper Info
Title
Recognizing software names in biomedical literature using machine learning.
Abstract
Software tools now are essential to research and applications in the biomedical domain. However, existing software repositories are mainly built using manual curation, which is time-consuming and unscalable. This study took the initiative to manually annotate software names in 1,120 MEDLINE abstracts and titles and used this corpus to develop and evaluate machine learning-based named entity recognition systems for biomedical software. Specifically, two strategies were proposed for feature engineering: (1) domain knowledge features and (2) unsupervised word representation features of clustered and binarized word embeddings. Our best system achieved an F-measure of 91.79% for recognizing software from titles and an F-measure of 86.35% for recognizing software from both titles and abstracts using inexact matching criteria. We then created a biomedical software catalog with 19,557 entries using the developed system. This study demonstrates the feasibility of using natural language processing methods to automatically build a high-quality software index from biomedical literature.
Year
DOI
Venue
2020
10.1177/1460458219869490
HEALTH INFORMATICS JOURNAL
Keywords
DocType
Volume
biomedical literature,biomedical software,biomedical software index,named entity recognition,natural language processing
Journal
26.0
Issue
ISSN
Citations 
SP1.0
1460-4582
1
PageRank 
References 
Authors
0.35
0
7
Name
Order
Citations
PageRank
Qiang Wei113330.22
Zhang Yaoyun25614.30
Muhammad Amith3229.01
Rebecca Lin431.14
Jenay Lapeyrolerie510.35
Cui Tao63512.77
Hua Xu765069.76