Abstract
This research investigates the challenges and effectiveness of various text representation methods (standard vector, grammar-based, and distributed), when applied to clustering short texts. The study explores Bag-of-Words for standard vector, Linguistic Inquiry and Word Count (LIWC), Part-of-Speech Tagging (POS-Tagging), and the Medical Research Council Psycholinguistic Database (MRC) for grammar-based, and Word2Vec, fastText, Doc2Vec, and SentenceBERT for distributed representations. Utilizing the aiNet bio-inspired clustering algorithm, the results reveal surprising findings, with grammar-based representations demonstrating competitive performance despite their simplicity, while standard vectors exhibit known challenges like high dimensionality. The study contributes insights into the properties of different text representations, providing a foundation for optimizing their application in clustering tasks with short and informal texts.