Proceedings of BioNLP 15

Abstract

high quality submissions, continuing the fine tradition of the preceding thirteen years of BioNLP. The high quality of the submissions ensured that 12 of those were accepted as full papers / oral presentations and 11 as short papers / poster presentations. The themes in this year's papers and posters show equal interest in clinical text and in biological language processing. The morning session and the keynote presentations focus on the latest developments in biomedical text processing, whereas the afternoon session will present innovations in clinical text processing. This year, researchers continue advancing pathway, event and relation extraction from the literature and information extraction from clinical text, as well as continuing research in languages other than English. KeynotesThe DARPA Big Mechanism Program Kevin Knight DARPA's Big Mechanism Program aims to develop automatic machine-reading technology to distill grounded, causal mechanisms from technical literature, and to assemble those mechanisms into a large, operational model. The first Big Mechanism domain is cancer biology. This talk will describe the goals of the program and the techniques being developed.Kevin Knight is a Senior Research Scientist and Fellow at the University of Southern California's Information Sciences Institute, and a Professor in the Computer Science Department at USC. He received a Ph.D. in computer science from Carnegie Mellon University and a bachelor's degree from Harvard University. His research interests include natural language processing, statistical modeling, machine translation, language generation, and code breaking.Machine Reading: Attempting to model and understand biological processes Christopher Manning Stanford UniversityMachine reading calls for programs that read and understand textual descriptions, whereas most current work only attempts to extract atomic facts, often from redundant web-scale corpora. Biological processes are an example of complex phenomena involving a series of events that are connected to one another through various relationships. This work focuses on these processes as a reading comprehension task that requires complex reasoning over a single document. The input is a paragraph describing a biological process, and the goal is to answer questions that require an understanding of the relations between entities and events in the process. To answer questions, we first try to extract from the paragrah a rich structure representing the events of the biological process and relations between them. We represent processes by graphs whose edges describe a set of causal and co-reference event-event relations, and characterize the structural properties of these graphs, so as to be able to better predict them from text descriptions. Then, we map the question to a formal query, which is executed against the extracted structure. AcknowledgmentsThe greatest debt owed by the organizers of a workshop like this is to the authors who graciously continue choosing BioNLP as the venue to share their truly insp...