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Co-occurrence graphs for word sense disambiguation in the biomedical domain

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Academic year: 2019

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Figure

Figure 1: Construction of the co-occurrence graph (part a) and disambiguation of a test instance (part b).
Figure 2: Example of disambiguation. Extraction of the target and context CUIs (top part) and comparison betweenthe disambiguation algorithm over the co-occurrence graph and the UMLS graph (bottom part).
Table 1: Statistics for the different test datasets: number of instances, number of ambiguous terms (or acronyms),minimum and maximum number of senses for a term and average number of senses per term.
Table 3: Comparative of results (accuracy in %) for state-of-the-art systems (see text) and the system reported in thiswork (Bio-Graph), for each of the different test datasets
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