Abstract

Abstract: The overwhelming size and rapid growth of the biomedical literature make it impossible for scientists to read all studies related to their work, potentially leading to missed connections and wasted time and resources.

We have developed LION -LBD, a literature-based discovery system that helps cancer researchers to make new discoveries from already published text. The system supports the idea of hypothesis generation and testing with the aid of text mining. The system is built with a particular focus on the molecular biology of cancer using state-of-the-art natural language processing, including named entity recognition and grounding to domain ontologies covering a wide range of entity types and a novel approach to detecting references to the hallmarks of cancer in text.

Bio: Simon Baker is a research associate (postdoc) at the Language Technology Lab (LTL). He also collaborates with the Natural Language and Information Processing (NLIP) group at the Computer Laboratory. His current research interests include: information extraction, text mining, and related applications such as automatic Literature-based Discovery (LBD). He works largely in the biomedical domain.