Multi-Label Text Classification

Multi-label text classification is an important task in natural language processing that allows each text instance to be assigned multiple labels rather than a single category. The goal of this task is to identify and extract all applicable labels from the given text, thereby achieving finer and more accurate content annotation. Multi-label text classification has broad application value in areas such as information retrieval, sentiment analysis, and news categorization, significantly enhancing the efficiency and accuracy of data processing and analysis.