Mining relations from the biomedical literature

Text mining deals with the automated annotation of texts and the extraction of facts from textual data for subsequent analysis. Such texts range from short articles and abstracts to large documents, for instance web pages and scientific articles, but also include textual descriptions in otherwise structured databases. This thesis focuses on two key problems in biomedical text mining: relationship extraction from biomedical abstracts —in particular, protein–protein interactions—, and a pre-requisite step, named entity recognition —again focusing on proteins. This thesis presents goals, challenges, and typical approaches for each of the main building blocks in biomedical text mining. We present out own approaches for named entity recognition of proteins and relationship extraction of proteinprotein interactions. For the first, we describe two methods, one set up as a classification task, the other based on dictionary-matching. For relationship extraction, we develop a methodology to automatically annotate large amounts of unlabeled data for relations, and make use of such annotations in a pattern matching strategy. This strategy first extracts similarities between sentences that describe relations, storing them as consensus patterns. We develop a sentence alignment approach that introduces multi-layer alignment, making use of multiple annotations per word. For the task of extracting protein-protein interactions, empirical results show that our methodology performs comparable to existing approaches that require a large amount of human intervention, either for annotation of data or creation of models.