No abstract

As the significant sub-task of information extraction, relation extraction aims to identify and classify semantic relations between pairs of entities and is widely adopted as the foundation of downstream applications including knowledge graphs, intelligent question answering, text mining and sentiment analysis. Different from general knowledge, domain knowledge is pertinent to specific fields which include a wealth of proprietary entities and relations. Besides, most of the data are formed as documents rather than sentences. In this paper, the task of domain relation extraction is defined, and the common domains are presented. Furthermore, we provide a systematic review of state-of-the-art techniques as well as the latest trends. We survey different neural network based techniques for relation extraction and describe the overall framework, training procedures, as well as the pros and cons of these techniques. Then, we introduce and compare the corpus and metrics used for domain relation extraction tasks. Finally, we conclude and discuss future research issues of domain relation extraction.