Naslov
Interactive and Iterative Annotation for Biomedical Entity Recognition

Autori
Yimam Seid Muhie, Biemann Chris, Majnaric Ljiljana , Sabanovic Sefket, Holzinger Andreas

Izvornik
Lecture Notes in Computer Science (0302-9743) BIH 2015 (2015), LNAI 9250; 347-357

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Interactive annotation Machine learning Knowledge discovery Data mining Human in the loop Biomedical entity recognition

Sažetak
In this paper, we demonstrate the impact of interactive machine learning for the development of a biomedical entity recognition dataset using a human-into-the-loop approach: during annotation, a machine learning model is built on previous annotations and used to propose labels for subsequent annotation. To demonstrate that such interactive and iterative annotation speeds up the development of quality dataset annotation, we conduct two experiments. In the first experiment, we carry out an iterative annotation experimental simulation and show that only a handful of medical abstracts need to be annotated to produce suggestions that increase annotation speed. In the second experiment, clinical doctors have conducted a case study in annotating medical terms documents relevant for their research. The experiments validate our method qualitatively and quantitatively, and give rise to a more personalized, responsive information extraction technology.

Izvorni jezik
Engleski

POVEZANOST RADA