Term Extraction

Automatic term extraction (ATE) is a natural language processing task focused on identifying key concepts and terms within specialized texts, aiding knowledge organization and information retrieval. Current research emphasizes the use of transformer-based neural models, often incorporating techniques like contrastive learning and term expansion to improve accuracy and efficiency, particularly in handling diverse and noisy data, including mathematical texts and legal documents. ATE's impact spans various fields, improving knowledge base construction, information retrieval systems, and downstream tasks such as machine translation and sentiment analysis, by facilitating more effective knowledge discovery and organization.