Jie Zhang

TL;DR: This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based relation extraction using SVM and illustrates that the base phrase chunking information is very effective for relation extraction and contributes to most of the performance improvement from syntactic aspect while additional information from full parsing gives limited further enhancement.

TL;DR: This study illustrates that the composite kernel can effectively capture both flat and structured features without the need for extensive feature engineering, and can also easily scale to include more features.

TL;DR: The PowerBioNE system is the first system which deals with the cascaded entity name phenomenon and the HMM and the k-NN algorithm outperform other models, such as back-off HMM, linear interpolated H MM, support vector machines, C4.5 rules and RIPPER, by effectively capturing the local context dependency and resolving the data sparseness problem.

TL;DR: A multi-criteria-based active learning approach is proposed and effectively applied to named entity recognition and includes all the criteria using two selection strategies, both of which result in less labeling cost than single-criterion-based method.

TL;DR: It is demonstrated that the epidermal/flexible PPG sensors are capable of continuously monitoring heart rate variability and precisely tracking the changes of pulse pressure at different postures of human subjects with the aid of electrocardiogram monitoring, exhibiting more reliable performance than commercial PPG sensor while consuming less power.