Lexical-semantic resources: yet powerful resources for automatic personality classification
This addresses the problem of improving personality classification in NLP by leveraging semantics, though it is incremental as it builds on existing feature-based approaches.
The paper tackled automatic personality classification by exploring lexical-semantic resources for word sense disambiguation and semantic categorization, achieving results comparable to state-of-the-art methods without needing personality-specific resources.
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.