Yoshihiro Ohta

Tuning support vector machines for biomedical named entity recognition

We explore the use of Support Vector Machines (SVMs) for biomedical named entity recognition. To make the SVM training with the available largest corpus - the GENIA corpus - tractable, we propose to split the non-entity class into sub-classes, using part-of-speech information. In addition, we explore new features such as word cache and the states of an HMM trained by unsupervised learning. Experiments on the GENIA corpus show that our class splitting technique not only enables the training with the GENIA corpus but also improves the accuracy. The proposed new features also contribute to improve the accuracy. We compare our SVM-based recognition system with a system using Maximum Entropy tagging method.

Speech analysing device

Computation of the partial correlation coefficients (PARCOR Ki) of a signal, using less cascaded hardware, is implemented by first deriving a sequence of auto-correlation coefficients (vj) which are then transformed into a sequence of Ki using a single section digital filter plus recirculating circuitry for data iteration.