Recent Advances in Hierarchical Multi-label Text Classification: A Survey
This is an incremental survey for researchers and practitioners in text classification, summarizing existing work without introducing new methods.
The paper surveys recent progress in hierarchical multi-label text classification, covering datasets, methods, evaluation metrics, learning strategies, and challenges, and suggests future research directions.
Hierarchical multi-label text classification aims to classify the input text into multiple labels, among which the labels are structured and hierarchical. It is a vital task in many real world applications, e.g. scientific literature archiving. In this paper, we survey the recent progress of hierarchical multi-label text classification, including the open sourced data sets, the main methods, evaluation metrics, learning strategies and the current challenges. A few future research directions are also listed for community to further improve this field.