A Mixture Language Model for Class-Attribute Mining from Biomedical Literature Digital Library

We define and study a novel text mining problem for biomedical literature digital library, referred to as the class-attribute mining. Given a collection of biomedical literature from a digital library addressing a set of objects (e.g., proteins) and their descriptions (e.g., protein functions), the tasks of class-attribute mining include: (1) to identify and summarize latent classes in the space of objects, (2) to discover latent attribute themes in the space of object descriptions, and (3) to summarize the commonalities and differences among identified classes along each attribute theme. We approach this mining problem through a mixture language model and estimate the parameters of the model using the EM algorithm. We demonstrate the effectiveness of the model with an application called protein community identification and annotation from Medline, the largest biomedical literature digital library with more than 16 millions abstracts.