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Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of alldoi:10.1016/j.ymeth.2014.11.020 pmid:25484339 fatcat:sb27mqklhzdu3jdp5pu5ug4spa