Abstract:
The absence of manually annotated training data presents an obstacle for the development of machine-learning based NLP tools in Indonesia. Existing annotation tools lack ...Show MoreMetadata
Abstract:
The absence of manually annotated training data presents an obstacle for the development of machine-learning based NLP tools in Indonesia. Existing annotation tools lack a mobile-friendly interface which is a problem in Indonesia where most users access the internet using their smartphone. In this paper, we propose the first mobile collaborative data annotation tool and evaluate it in an experiment involving 15 Indonesian students who annotated 1500 data records using their smartphones. Users confirmed the responsiveness and good usability of the system. We observed that using the mobile tool users tend to annotate data in multiple short sessions of 7-15 minutes rather than in a single long one. Future research could examine ways to increase inter-annotator agreement, which was moderate in our experiment.
Date of Conference: 21-23 November 2016
Date Added to IEEE Xplore: 13 March 2017
ISBN Information: