UCLA

The world is overflowing with text. This ever-growing resource has the ability to capture thoughts, ideas, and understandings. One example is the scientific research paper which often contains a new discovery or details an in depth understanding---and new research papers are growing at an exponential rate, a rate that may be difficult for the human mind to keep up with. Additionally, the increase of collaboration across different knowledge domains requires an increased effort for a specialist of any one field to understand. These increases can lead to mistakes among researchers from missing important information that has already been documented. Similarly, the electronic health record is increasing rapidly as technology is integrating itself into the patient physician interaction. To be able to deliver information quickly and accurately to a physician can help ease the burden and lesson the mistakes that a primary care physician can make when dealing with the increasing pressure from seeing too many patients in too little time. Given the enormous amount of textual data, computational techniques must be developed that can effectively process the data. This work presents approaches that seek knowledge discovery from a large input of biomedical and scientific text. In the context of scientific research papers, we discuss how and why we need to automate the scientific method using a causal pipeline. Starting with the raw text of a scientific corpus we demonstrate the ability to improve scientific decision making in experiment planning and deductions. For the task of summarizing corpora, we introduce a new topic model that seeks to model topics off a pre-existing knowledge source. As we show empirically, our methods of extraction, connection, and summarization of relevant electronic text records results in knowledge discovery and new understandings.