97,852 research outputs found

    Using natural language processing to improve biomedical concept normalization and relation mining

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    This thesis concerns the use of natural language processing for improving biomedical concept normalization and relation mining. We begin with introducing the background of biomedical text mining, and subsequently we will continue by describing a typical text mining pipeline, some key issues and problems in mining biomedical texts, and the possibility of using natural language procesing to solve the problems. Finally we end an outline of the work done in this thesis

    Ontology-assisted database integration to support natural language processing and biomedical data-mining

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    Successful biomedical data mining and information extraction require a complete picture of biological phenomena such as genes, biological processes, and diseases; as these exist on different levels of granularity. To realize this goal, several freely available heterogeneous databases as well as proprietary structured datasets have to be integrated into a single global customizable scheme. We will present a tool to integrate different biological data sources by mapping them to a proprietary biomedical ontology that has been developed for the purposes of making computers understand medical natural language

    Using Distributed Representations to Disambiguate Biomedical and Clinical Concepts

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    In this paper, we report a knowledge-based method for Word Sense Disambiguation in the domains of biomedical and clinical text. We combine word representations created on large corpora with a small number of definitions from the UMLS to create concept representations, which we then compare to representations of the context of ambiguous terms. Using no relational information, we obtain comparable performance to previous approaches on the MSH-WSD dataset, which is a well-known dataset in the biomedical domain. Additionally, our method is fast and easy to set up and extend to other domains. Supplementary materials, including source code, can be found at https: //github.com/clips/yarnComment: 6 pages, 1 figure, presented at the 15th Workshop on Biomedical Natural Language Processing, Berlin 201
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