Automatic detection of basic level categories [Temel seviye kategorilerin otomatik tespiti]

Official URL: https://dx.doi.org/10.1109/SIU66497.2025.11112453

Basic level categories (BLCs), which can be defined as the most inclusive level at which a concrete mental image of the entire category can be formed, have proven to be useful in a variety of applications in natural language processing (NLP) and computer vision (CV) tasks, such as word sense disambiguation, image searches, image description, and retrieval. Limiting their practical applications, current methods for detecting BLCs predominantly rely on rule-based methods and external knowledge sources rather than the information extracted directly from the text. In this manuscript, we propose a novel approach to detect BLCs that is merely based on the information obtained from the word embeddings, including Gaussian word embeddings (W2G) and embeddings retrieved from transformer-based models such as BERT and GPT-2. The proposed method significantly outperforms existing works in performance and practicality, demonstrating the effectiveness of contextual word embeddings for BLC detection.