@prefix foaf: . @prefix ns1: . @prefix ns2: . ns1:harith-alani foaf:made ns2:iswc-2017-iswc2017-research-322 . ns1:gregoire-burel foaf:made ns2:iswc-2017-iswc2017-research-322 . ns1:hassan-saif foaf:made ns2:iswc-2017-iswc2017-research-322 . @prefix ns3: . @prefix ns4: . ns4:proceedings ns3:hasPart ns2:iswc-2017-iswc2017-research-322 . @prefix rdf: . @prefix ns6: . ns2:iswc-2017-iswc2017-research-322 rdf:type ns6:ProceedingsPaper . @prefix owl: . ns2:iswc-2017-iswc2017-research-322 rdf:type owl:Thing . @prefix ns8: . ns2:iswc-2017-iswc2017-research-322 rdf:type ns8:InformationObject , ns8:Object . @prefix rdfs: . ns2:iswc-2017-iswc2017-research-322 rdf:type rdfs:Resource , ns8:SocialObject , ns3:InProceedings ; rdfs:label "Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media" . @prefix ns10: . ns2:iswc-2017-iswc2017-research-322 owl:sameAs ns10:iswc-2017-iswc2017-research-322 . @prefix ns11: . ns2:iswc-2017-iswc2017-research-322 ns11:abstract "When crises hit, many flog to social media to share or consume information related to the event. Social media posts during crises tend to provide valuable reports on affected people, donation offers, help requests, advice provision, etc. Automatically identifying the category of information (e.g., reports on affected individuals, donations and volunteers) contained in these posts is vital for their efficient handling and consumption by effected communities and concerned organisations. In this paper, we introduce Sem-CNN; a wide and deep Convolutional Neural Network (CNN) model designed for identifying the category of information contained in crisis-related social media content. Unlike previous models, which mainly rely on the lexical representations of words in the text, the proposed model integrates an additional layer of semantics that represents the named entities in the text, into a wide and deep CNN network. Results show that the Sem-CNN model consistently outperforms the baselines which consist of statistical and non-semantic deep learning models." . @prefix dc: . ns2:iswc-2017-iswc2017-research-322 dc:creator ns1:hassan-saif , ns1:gregoire-burel , ns1:harith-alani . @prefix xsd: . ns2:iswc-2017-iswc2017-research-322 dc:subject "Crisis Information Processing"^^xsd:string , "Social Media"^^xsd:string , "Semantic Deep Learning"^^xsd:string ; dc:title "Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media" . @prefix ns14: . @prefix ns15: . ns2:iswc-2017-iswc2017-research-322 ns14:authorList ns15:iswc-2017-iswc2017-research-322 ; foaf:maker ns1:harith-alani , ns1:gregoire-burel , ns1:hassan-saif ; ns3:abstract "When crises hit, many flog to social media to share or consume information related to the event. Social media posts during crises tend to provide valuable reports on affected people, donation offers, help requests, advice provision, etc. Automatically identifying the category of information (e.g., reports on affected individuals, donations and volunteers) contained in these posts is vital for their efficient handling and consumption by effected communities and concerned organisations. In this paper, we introduce Sem-CNN; a wide and deep Convolutional Neural Network (CNN) model designed for identifying the category of information contained in crisis-related social media content. Unlike previous models, which mainly rely on the lexical representations of words in the text, the proposed model integrates an additional layer of semantics that represents the named entities in the text, into a wide and deep CNN network. Results show that the Sem-CNN model consistently outperforms the baselines which consist of statistical and non-semantic deep learning models." ; ns3:hasAuthorList ns15:iswc-2017-iswc2017-research-322 ; ns3:isPartOf ns4:proceedings ; ns3:keyword "Crisis Information Processing"^^xsd:string , "Semantic Deep Learning"^^xsd:string , "Social Media"^^xsd:string ; ns3:title "Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media" .