A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers
It provides a comprehensive overview for researchers in NLP and information retrieval, but it is incremental as it synthesizes existing work rather than introducing new methods.
This survey reviews recent advances in relation extraction (RE), focusing on deep learning techniques and pre-trained language models that have improved state-of-the-art performance, though no specific numerical results are provided as it is a review paper.
Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and information retrieval applications, such as knowledge graph completion and question answering. In recent years, deep neural networks have dominated the field of RE and made noticeable progress. Subsequently, the large pre-trained language models have taken the state-of-the-art RE to a new level. This survey provides a comprehensive review of existing deep learning techniques for RE. First, we introduce RE resources, including datasets and evaluation metrics. Second, we propose a new taxonomy to categorize existing works from three perspectives, i.e., text representation, context encoding, and triplet prediction. Third, we discuss several important challenges faced by RE and summarize potential techniques to tackle these challenges. Finally, we outline some promising future directions and prospects in this field. This survey is expected to facilitate researchers' collaborative efforts to address the challenges of real-world RE systems.