Word Similarity

Word similarity research focuses on computationally modeling how humans perceive the relatedness of words, aiming to improve natural language processing tasks and enhance our understanding of semantic cognition. Current research explores various approaches, including leveraging contextual information with machine translation techniques and refining pre-trained language models like BERT and ELECTRA to generate more accurate and efficient word embeddings. These advancements are crucial for improving semantic textual similarity tasks, enriching psycholinguistic studies with computationally grounded datasets, and addressing challenges like handling rare or newly emerging scientific terminology through knowledge graph integration.