<?xml version="1.0" encoding="UTF-8"?><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcq="http://purl.org/dc/terms/"><records count="1" morepages="false" start="1" end="1"><record rownumber="1"><dc:product_type>Conference Paper</dc:product_type><dc:title>Semi-supervised Multi-instance Interpretable Models for Flu Shot Adverse Event Detection</dc:title><dc:creator>Wang, Junxiang; Zhao, Liang; Ye, Yanfang</dc:creator><dc:corporate_author/><dc:editor/><dc:description/><dc:publisher/><dc:date>2018-01-01</dc:date><dc:nsf_par_id>10106503</dc:nsf_par_id><dc:journal_name>International Conference on Big Data 2018</dc:journal_name><dc:journal_volume/><dc:journal_issue/><dc:page_range_or_elocation>851 to 860</dc:page_range_or_elocation><dc:issn/><dc:isbn/><dc:doi>https://doi.org/10.1109/BigData.2018.8622434</dc:doi><dcq:identifierAwardId>1755850; 2103745</dcq:identifierAwardId><dc:subject/><dc:version_number/><dc:location/><dc:rights/><dc:institution/><dc:sponsoring_org>National Science Foundation</dc:sponsoring_org></record></records></rdf:RDF>