Biomedical Named Entity Recognition Based on the Combination of Regional and Global Text Features

The biomedical information extraction, especially Named Entity Recognition (NER), is a primary task in biomedical text-mining due to the rapid growth of large-scale literature. Extracting biomedical entities aims at identifying specific entities (words or phrases) from those unstructured text data. In this work, we introduce a novel biomedical NER system utilizing a combination of regional and global text features: linguistic, lexical, contextual, and syntactic features. Our system adopts Conditional Random Fields (CRFs) [1] as a machine learning algorithm and consists of two major pipelines (see Figure 1). We especially focus on constructing the first pipeline for text processing in a modularized manner and discovering rich feature sets regarding comprehensive linguistics and contexts. To implement the CRF framework in the second pipeline, our system uses a modified version of Mallet [2] to take advantage of feature induction. As a result of 10-fold cross-validation, our system achieves from 0.99% up to 18.47% of F-measure improvement as well as the highest precision compared to existing open-source biomedical NER systems on GENETAG corpus [3]. We figure out that several components such as abundant key features, external resources, and feature induction contribute to the performance of the proposed system.