Lexical-semantic resources: yet powerful resources for automatic personality classification

Xuan-Son Vu, Lucie Flekova, Lili Jiang, Iryna Gurevych


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.
Anthology ID:
2018.gwc-1.20
Volume:
Proceedings of the 9th Global Wordnet Conference
Month:
January
Year:
2018
Address:
Nanyang Technological University (NTU), Singapore
Venue:
GWC
SIG:
Publisher:
Global Wordnet Association
Note:
Pages:
172–181
Language:
URL:
https://aclanthology.org/2018.gwc-1.20
DOI:
Bibkey:
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PDF:
https://aclanthology.org/2018.gwc-1.20.pdf