Word Learning

Word learning research investigates how humans and machines acquire word meanings, focusing on efficient methods for associating words with their visual and contextual representations. Current research explores improved word embedding models like Word2Vec, incorporating distance weighting and dynamic window sizes to enhance accuracy, and leverages multimodal approaches combining visual and textual data, particularly in low-data regimes. These advancements aim to create more robust and human-like word learning systems, with implications for natural language processing, computer vision, and developmental psychology by providing insights into both human and artificial language acquisition.