Text Similarity

Text similarity research focuses on developing methods to automatically quantify the semantic relatedness between text segments, aiming to improve information retrieval, question answering, and other NLP tasks. Current research emphasizes the development and evaluation of novel similarity metrics, often leveraging advanced architectures like transformer-based models (e.g., BERT, RoBERTa) and graph-based approaches, as well as exploring the use of large language models for data generation and evaluation. These advancements are driving improvements in various applications, including automated document analysis, personalized content generation, and cross-lingual information retrieval. The field is also actively addressing challenges such as handling noisy text, incorporating syntactic information, and ensuring robustness across diverse languages and domains.