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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)In: Proceedings of the 9th Global WordNet Conference (GWC 2018) / [ed] Francis Bond, Takayuki Kuribayashi, Christiane Fellbaum, Piek Vossen, Singapore: Nanyang Technological University (NTU) , 2018, , p. 10p. 173-182Conference paper, Published paper (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. 10p. 173-182
Keywords [en]
Personality Profiling
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-144672ISBN: 978-981-11-7087-4 (print)OAI: oai:DiVA.org:umu-144672DiVA, id: diva2:1181847
Conference
The 9th Global WordNet Conference GWC2018, Singapore, January 8-12, 2018
Projects
Privacy-aware Data FederationAvailable from: 2018-02-09 Created: 2018-02-09 Last updated: 2019-08-22Bibliographically approved

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Vu, Xuan-SonLili, Jiang

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CiteExportLink to record
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