Scientific publications are the main media through which researchers report their new findings. The huge amount and the continuing rapid growth of the number of published articles in biomedicine, has made it particularly difficult for researchers to access and utilize the knowledge contained in them. Currently, there are over 21 million publications indexed in PubMed, which is the main system that provides access to the biomedical literature. Over 2000 new entries are added to the system every day. Developing text mining techniques to automatically extract biologically important information such as relationships between biomolecules is not only useful, but also necessary to facilitate biomedical research and to speed-up scientific progress. Most of the prior studies in the biomedical text mining field tackle the problem of extracting the fact that there is a relationship between a pair of biomolecules. However, for extracted information to make sense, a great deal of biological context is required. While some of this context such as relationship type and directionality is found in the sentence that actually reports the relationship, some of it such as species and experimental method is likely stated elsewhere in the article. The goal of the proposed project is to design methods based on natural language processing and machine learning to extract relationships among biomolecules and their local (sentence-level) and non-local (document-level) context information, as well as to design novel knowledge discovery methods that utilize the extracted contextual information