Automatically identifying health- and clinical-related content in wikipedia.
Physicians are increasingly using the Internet for finding medical information related to patient care. Wikipedia is a valuable online medical resource to be integrated into existing clinical question answering (QA) systems. On the other hand, Wikipedia contains a full spectrum of world's knowledge and therefore comprises a large partition of non-health-related content, which makes disambiguation more challenging and consequently leads to large overhead for existing systems to effectively filter irrelevant information. To overcome this, we have developed both unsupervised and supervised approaches to identify health-related articles as well as clinically relevant articles. Furthermore, we explored novel features by extracting health related hierarchy from the Wikipedia category network, from which a variety of features were derived and evaluated. Our experiments show promising results and also demonstrate that employing the category hierarchy can effectively improve the system performance.
NLP R&D, Nuance Communication Inc., Burlington, MA, USA.
Feifan
F
Liu
Soheil
S
Moosavinasab
Shashank
S
Agarwal
Andrew S
AS
Bennett
Hong
H
Yu
D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D004690:Encyclopedias as Topic; D063025:Health Information Management / Q000379:methods*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D061108:Social Media; D018875:Vocabulary, Controlled