@prefix foaf: . @prefix ns1: . @prefix ns2: . ns1:yong-yu foaf:made ns2:iswc-2018-research-83 . ns1:kewei-tu foaf:made ns2:iswc-2018-research-83 . ns1:shu-rong foaf:made ns2:iswc-2018-research-83 . ns1:weinan-zhang foaf:made ns2:iswc-2018-research-83 . @prefix ns3: . @prefix ns4: . ns4:proceedings ns3:hasPart ns2:iswc-2018-research-83 . @prefix rdf: . ns2:iswc-2018-research-83 rdf:type ns3:InProceedings . @prefix rdfs: . ns2:iswc-2018-research-83 rdfs:label "QA4IE: A Question Answering based Framework for Information Extraction" . @prefix dc: . ns2:iswc-2018-research-83 dc:creator ns1:lin-qiu , ns1:lihua-qian , ns1:dongyu-ru , ns1:weinan-zhang , ns1:kewei-tu , ns1:yanru-qu , ns1:yong-yu , ns1:shu-rong , ns1:suoheng-li , ns1:hao-zhou ; ns3:abstract "Information Extraction (IE) refers to automatically extracting structured relation tuples from unstructured texts. Common IE solutions, including Relation Extraction (RE) and open IE systems, can hardly handle cross-sentence tuples, and are severely restricted by limited relation types as well as informal relation specifications (e.g., free-text based relation tuples). In order to overcome these weaknesses, we propose a novel IE framework named QA4IE, which leverages the flexible question answering (QA) approaches to produce high quality relation triples across sentences. Based on the framework, we develop a large IE benchmark with high quality human evaluation. This benchmark contains 293K documents, 2M golden relation triples, and 636 relation types. We compare our system with some IE baselines on our benchmark and the results show that our system achieves great improvements." . @prefix ns8: . ns2:iswc-2018-research-83 ns3:hasAuthorList ns8:iswc-2018-research-83 ; ns3:isPartOf ns4:proceedings . @prefix xsd: . ns2:iswc-2018-research-83 ns3:keyword "Information Extraction"^^xsd:string , "Natural Language Processing"^^xsd:string , "Deep Learning"^^xsd:string ; ns3:title "QA4IE: A Question Answering based Framework for Information Extraction" . ns1:dongyu-ru foaf:made ns2:iswc-2018-research-83 . ns1:hao-zhou foaf:made ns2:iswc-2018-research-83 . ns1:lihua-qian foaf:made ns2:iswc-2018-research-83 . ns1:lin-qiu foaf:made ns2:iswc-2018-research-83 . ns1:suoheng-li foaf:made ns2:iswc-2018-research-83 . ns1:yanru-qu foaf:made ns2:iswc-2018-research-83 . @prefix ns10: . ns10:iswc2018-author-dongyu-ru-iswc-2018-research-83 ns3:withDocument ns2:iswc-2018-research-83 . ns10:iswc2018-author-hao-zhou-iswc-2018-research-83 ns3:withDocument ns2:iswc-2018-research-83 . ns10:iswc2018-author-kewei-tu-iswc-2018-research-83 ns3:withDocument ns2:iswc-2018-research-83 . ns10:iswc2018-author-lihua-qian-iswc-2018-research-83 ns3:withDocument ns2:iswc-2018-research-83 . ns10:iswc2018-author-lin-qiu-iswc-2018-research-83 ns3:withDocument ns2:iswc-2018-research-83 . ns10:iswc2018-author-shu-rong-iswc-2018-research-83 ns3:withDocument ns2:iswc-2018-research-83 . ns10:iswc2018-author-suoheng-li-iswc-2018-research-83 ns3:withDocument ns2:iswc-2018-research-83 . ns10:iswc2018-author-weinan-zhang-iswc-2018-research-83 ns3:withDocument ns2:iswc-2018-research-83 . ns10:iswc2018-author-yanru-qu-iswc-2018-research-83 ns3:withDocument ns2:iswc-2018-research-83 . ns10:iswc2018-author-yong-yu-iswc-2018-research-83 ns3:withDocument ns2:iswc-2018-research-83 .