Word Vector

Word vectors are numerical representations of words, capturing semantic meaning and relationships within a vector space. Current research focuses on improving efficiency by reducing vector dimensionality, exploring different model architectures like GloVe, CNN-BiLSTM, and transformers for enhanced performance in tasks such as text classification and zero-shot learning, and investigating methods to improve the quality and robustness of word vector generation, including the use of large language models. These advancements have significant implications for various natural language processing applications, including improved text classification, information retrieval, and cross-lingual tasks.