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Social Relation Extraction from Chatbot Conversations: A Shortest Dependency Path Approach

dc.contributor.authorGlas, Markus
dc.contributor.editorBecker, Michael
dc.date.accessioned2019-10-14T12:09:06Z
dc.date.available2019-10-14T12:09:06Z
dc.date.issued2019
dc.description.abstractDigital dialog systems, also known as chatbots, often lack in the sense of a human-like and individualized interaction. The ability to learn someoneŠs social relations during conversations can lead to more personal responses and therefore to a more human-like and diverse conversation. In this work we present S-REX, a comparison method for extracting social relations from chatbot conversations. The implemented approach uses information from the shortest dependency path in combination with state-of-the-art natural language processing models for entity recognition and semantic word vectors. The method is evaluated on two conversational datasets and achieves results close to more complex neural network methods without the need of extensive training.en
dc.identifier.isbn978-3-88579-449-3
dc.identifier.pissn1614-3213
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/28989
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSKILL 2019 - Studierendenkonferenz Informatik
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Seminars, Volume S-15
dc.subjectRelation Extraction
dc.subjectInformation Extraction
dc.subjectChatbots
dc.subjectInformation Retrieval
dc.subjectText Mining
dc.subjectNatural Language Processing
dc.titleSocial Relation Extraction from Chatbot Conversations: A Shortest Dependency Path Approachen
dc.typeText/Conference Paper
gi.citation.endPage22
gi.citation.publisherPlaceBonn
gi.citation.startPage11
gi.conference.date25.-26. September 2019
gi.conference.locationKassel
gi.conference.sessiontitleNatural Language Processing

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