Breast Cancer Using KMSVM Techniques In Wisconsin Detect Prognostic Breast Cancer Data Sets

Abstract

Event extraction is a particularly challenging type of information extraction. Most current event extraction systems rely on local information at the phrase or sentence level. However, this local context may be insufficient to resolve ambiguity in identifying particular types of events; information from a wider scope can serve to resolve some of this ambiguity. In this paper, we first investigate how to extract supervised and unsupervised features to improve a supervised baseline system. Then, we present two additional tasks to show the benefit of wider scope features in semi-supervised learning and active learning. Experiments show that using features from wider scope can not only aid a supervised local event extraction baseline system, but also help the semi-supervised or active learning approach. The resulting efficient nugget pool is used to guide users’ exploration. Among the five stages of NMS framework, we pay our main attention on solving the technical challenges existed in nugget combination and refinement.