CGM: A biomedical text categorization approach using concept graph mining
Said Bleik, Min Song, Aaron Smalter, Jun Huan, Gerald Henry Lushington · 2009
Text Categorization is used to organize and manage biomedical text databases that are growing at an exponential rate. Feature representations for documents are a crucial factor for the performance of text categorization. Most of the successful existing techniques use a vector representation based on key entities extracted from the text. In this paper we investigate a new direction where we represent a document as a graph. In this representation we identify high level concepts and build a rich graph structure that contains additional concepts and relationships. We then use graph kernel techniques to perform text categorization. The results show a significant improvement in accuracy when compared to categorization based on only the extracted concepts.