Temporal Expression

Temporal expression (TE) processing focuses on automatically identifying and interpreting time-related information within text, aiming to improve natural language understanding for various applications. Current research emphasizes developing more efficient and multilingual TE identification and normalization systems, often employing deep learning models like masked language models and leveraging large pre-trained language models through techniques such as in-context learning. These advancements address limitations of rule-based approaches, particularly in handling diverse languages and non-standard temporal expressions, leading to improved performance in tasks like question answering and information retrieval. The availability of larger, multilingual datasets and open-source tools is fostering collaboration and accelerating progress in this field.