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first part, we eciently recognized entities by establishing regular expressions
and using Mesh ontology. In the second part, we extracted HLA-disease inter-
action information in sentence of complex structure by searching parse trees.
We extracted relation information using 909 abstracts in PubMed and offered
the information at our web site. Then, we tested the algorithm with 144 ran-
domly selected sentences. The precision rates reported 89.6% and reported 57.4%
in summarization of these sentences. Our algorithm may be extended to other
medicine fields such as mental disease and asthma where the relationship between
gene and disease is also of importance. We will continue to research an automatic
filtering method using machine learning technologies to filter sentences that have
no relation between entities without relation and filtering keywords.
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Dissertation, University of Pennsylvania (1999)
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