Review of Text-Oriented Entity Relation Extraction Research

Abstract

Information extraction is the foundation of knowledge graph construction, and relation extraction, as a key process and core step of information extraction, aims to locate entities from text data and recognize semantic links between entities. Therefore, improving the efficiency of relation extraction can effectively improve the quality of information extraction, which affects the construction of knowledge graph and subsequent downstream tasks. Relation extraction can be categorized into sentence-level relation extraction and document-level relation extraction according to the length of the extracted text. The two levels of extraction methods have their own advantages and disadvantages in different application scenarios: sentence-level relation extraction is suitable for application scenarios with smaller datasets, while document-level relation extraction is suitable for scenarios such as news event analysis, long reports or articles with relational mining. Unlike the existing relation extraction, this paper first introduces the basic concept of relation extraction and the development history of the field in recent years, lists the datasets used in the two levels of relation extraction, and gives an overview of the characteristics of the datasets. Then, this paper elaborates on the sentence-level relation extraction and the document-level relation extraction respectively, summarizes the advantages and disadvantages of different levels of relation extraction, and analyses the performance and limitations of the representative models in each method. Finally, this paper summarizes the problems in the current research field and looks forward to future development of relation extraction.

Keywords

• information extraction; entity relation extraction; sentence-level relation extraction; document-level relation extraction; knowledge graph construction