1 Introduction
With the exponential explosion of biomedical literature, such as MEDLINE, developing automatic text mining tools has become essential for people to seek information more accurately and efficiently. Biomedical text miming (BioTM) [12] becomes a hot area in data mining. The fundamental tasks, such as named entity recognition (NER), protein-protein interaction extraction (PPIE) and text classification (TC) have attracted a lot of research interests in various domains including Bioinformatics, natural language processing (NLP), and machine learning (ML). Although these tasks focus on extracting information of different formats, e.g., entities, relations or documents, classical methods usually treat them as the classification of text snippets and the methodologies have a lot in common. In traditional methods, each example is represented by a feature vector where each element is generated by a Boolean function indicating whether a word, n-gram or lexical pattern appears in the current example, and then these features are integrated in a supervised learning framework.