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:
- Cite (ACL):
- Xuan-Son Vu, Lucie Flekova, Lili Jiang, and Iryna Gurevych. 2018. Lexical-semantic resources: yet powerful resources for automatic personality classification. In Proceedings of the 9th Global Wordnet Conference, pages 172–181, Nanyang Technological University (NTU), Singapore. Global Wordnet Association.
- Cite (Informal):
- Lexical-semantic resources: yet powerful resources for automatic personality classification (Vu et al., GWC 2018)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2018.gwc-1.20.pdf