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http://dx.doi.org/10.4275/KSLIS.2017.51.4.077

A Comparative Study on Deep Learning Topology for Event Extraction from Biomedical Literature  

Kim, Seon-Wu (경기대학교 일반대학원 문헌정보학과)
Yu, Seok Jong (한국과학기술정보연구원 생명의료융합기술연구실)
Lee, Min-Ho (한국과학기술정보연구원 생명의료HPC연구센터)
Choi, Sung-Pil (경기대학교 문헌정보학과)
Publication Information
Journal of the Korean Society for Library and Information Science / v.51, no.4, 2017 , pp. 77-97 More about this Journal
Abstract
A recent sharp increase of the biomedical literature causes researchers to struggle to grasp the current research trends and conduct creative studies based on the previous results. In order to alleviate their difficulties in keeping up with the latest scholarly trends, numerous attempts have been made to develop specialized analytic services that can provide direct, intuitive and formalized scholarly information by using various text mining technologies such as information extraction and event detection. This paper introduces and evaluates total 8 Convolutional Neural Network (CNN) models for extracting biomedical events from academic abstracts by applying various feature utilization approaches. Also, this paper conducts performance comparison evaluation for the proposed models. As a result of the comparison, we confirmed that the Entity-Type-Fully-Connected model, one of the introduced models in the paper, showed the most promising performance (72.09% in F-score) in the event classification task while it achieved a relatively low but comparable result (21.81%) in the entire event extraction process due to the imbalance problem of the training collections and event identify model's low performance.
Keywords
Biomedical Event; Event Extraction; Information Extraction; Natural Language Processing(NLP); Deep-Learning;
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