Title:     CRFs model and its application in biomedical NER

Speaker:   Sun Chengjie

Abstract:  This research present a method using Conditional Random Fields
model involving rich features to extract biomedical named entities from
biomedical literature. Shallow syntactic features are first introduced to
Conditional Random Fields model and do boundary detection and semantic
labeling at the same time, which effectively improve the model's
performance. Experiments show that our method can achieve an F-measure of
71.2% in JNLPBA test data and which is better than most of state-of-the-art
system.