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Lexical-semantic resources: yet powerful resources for automatic personality classification
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Database and Data Mining)ORCID iD: 0000-0001-8820-2405
(Amazon Research Germany)
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Database and Data Mining)
(UKP Lab, Computer Science Department, Technische Universitat Darmstadt)
2018 (English)Conference proceedings (editor) (Refereed)
Abstract [en]

In this paper, we aim to reveal the impact of lexical-semantic resources, used in particular for word sense disambiguation and sense-level semantic categorization, on automatic personality classification task. While stylistic features (e.g., part-of-speech counts) have been shown their power in this task, the impact of semantics beyond targeted word lists is relatively unexplored. We propose and extract three types of lexical-semantic features, which capture high-level concepts and emotions, overcoming the lexical gap of word n-grams. Our experimental results are comparable to state-of-the-art methods, while no personality-specific resources are required.

Place, publisher, year, edition, pages
Singapore: Nanyang Technological University (NTU) , 2018. , p. 10
Series
Proceeding of The 9th Global WordNet Conference GWC2018
Keyword [en]
Personality Profiling
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-144672OAI: oai:DiVA.org:umu-144672DiVA, id: diva2:1181847
Conference
The 9th Global WordNet Conference GWC2018
Projects
Privacy-aware Data Federation
Available from: 2018-02-09 Created: 2018-02-09 Last updated: 2018-06-09

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Vu, Xuan-Son

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
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Output format
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  • asciidoc
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