Document Ranking with Citation Information and Oversampling Sentence Classification in the LUIMA Framework
Apoorva Bansal, Zheyuan Bu, Biswajeet Mishra, Silun Wang, Kevin D. Ashley, Matthias Grabmair · Frontiers in artificial intelligence and applications · 2016
We report on prototype experiments expanding on prior work [2] in retrieving and ranking vaccine injury decisions using semantic information and classifying sentences as legal rules or findings about vaccine-injury causation. Our positive results include that query element coverage features and aggregate citation information using a BM25-like score can improve ranking results, and that larger amounts of annotated sentence data improve classification performance. Negative observations include that LUIMA-specific sentence features do not impact sentence classification, and that synthetic oversampling improves classification only for the sparser of the two predicted sentence types.