Lexical Representation

Lexical representation focuses on how words and their meanings are encoded and processed, aiming to create effective and interpretable models for various natural language processing tasks. Current research emphasizes developing hybrid models that combine sparse lexical representations (efficient for retrieval) with dense semantic representations (capturing nuanced meaning), often leveraging large language models like BERT and Llama 2, and employing techniques like integrated gradients for explainability. These advancements improve information retrieval, cross-modal alignment (e.g., image-text), and applications such as emotion detection in speech, ultimately enhancing the understanding and utilization of language in diverse fields.