  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.
