An Ensemble method for Content Selection for Data-to-text Systems. (arXiv:1506.02922v1 [cs.CL])

We present a novel approach for automatic report generation from time-series data, in the context of student feedback generation. Our proposed methodology treats content selection as a multi-label classification (MLC) problem, which takes as input time-series data (students' learning data) and outputs a summary of these data (feedback). Unlike previous work, this method considers all data simultaneously using ensembles of classifiers, and therefore, it achieves higher accuracy and F- score compared to meaningful baselines.

from cs.AI updates on arXiv.org http://ift.tt/1FHM93C
via IFTTT