Contextual Similarity

Contextual similarity focuses on measuring the similarity between data points (words, images, legal cases) considering their surrounding context, aiming to improve information retrieval and related tasks. Current research employs deep learning models, particularly neural networks and transformer architectures, often incorporating techniques like metric learning and knowledge distillation to enhance the accuracy and robustness of similarity measures. This work has significant implications for various fields, improving efficiency in tasks such as legal research, image retrieval, and natural language processing applications like spell checking and dialogue systems by leveraging contextual information for more accurate and relevant results.