@inproceedings{vu-etal-2018-lexical,
    title = "Lexical-semantic resources: yet powerful resources for automatic personality classification",
    author = "Vu, Xuan-Son  and
      Flekova, Lucie  and
      Jiang, Lili  and
      Gurevych, Iryna",
    editor = "Bond, Francis  and
      Vossen, Piek  and
      Fellbaum, Christiane",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2018.gwc-1.20/",
    pages = "172--181",
    abstract = "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."
}