CRFs based parallel biomedical named entity recognition algorithm employing MapReduce framework

As the rapid growth of the biomedical literature, the model training time in biomedical named entity recognition increases sharply when dealing with large-scale training samples. How to increase the efficiency of named entity recognition in biomedical big data becomes one of the key problems in biomedical text mining. For the purposes of improving the recognition performance and reducing the training time, this paper proposes an optimization method for two-phase recognition using conditional random fields. In the first stage, each named entity boundary is detected to distinguish all real entities. In the second stage, we label the semantic class of the entity detected. To expedite the training speed, in these two phases, we implement the model training process on a parallel optimization program framework based on MapReduce. Through dividing the training set into several parts, the iterations in the training algorithm are designed as map tasks which can be executed simultaneously in a cluster, where each map function is designed to complete the calculation of a gradient vector component for each part in the training set. Our experiments show that the proposed method in this paper can achieve high performance with short training time, which has important implications for the current biological big data processing.