Extracting Terms from Texts with Conditional Random Fields

Abstract

The rapid growing of biological text has promoted the research of the text mining of various non structured documents with the emphasis on the mining of biological knowledge. At the same time, the majority of biological text mining efforts based on the identification of the name of the biological term gene and protein. Therefore, how to recognize the biological terms effectively from the text has become one of the important issues in bioinformatics. Conditional random fields, an important machine learning algorithm, is a model of the probability of a graph model to give an opinion of the label. They traditionally use a set of observations and labels to receive training. Here we use controlled release fertilizer for a class of temporal learning algorithms, in reinforcement learning. Therefore tags are operating, updated environment, the impact of the next observation. Thus, from reinforcement learning, the controlled release fertilizer provides a model of joint action in the decentralized Markov decision process, and defines how agents can communicate with each other, and choose the best way of joint action. We use the hot data corpus for training and testing. The results show that the system can effectively find out the biological terms from the text. We get along with the average accuracy of rate=90.8%, the average recall of rate=90.6%, and the average rate=90.6% F1 six category of biological terms. The results are quite good for the entity recognition system, which is named after many other biological organisms.