Entity disambiguation is the process of chaining an identified entity referent to its corresponding entry in a specific knowledge base.The task of entity disambiguation is to solve the word polysemy problem where a named entity referent term corresponds to multiple entity concepts based on contextual information,and it plays an important role in the construction of knowledge graphs for accurate extraction of information from massive data,which is a fundamental task in natural language processing.We mainly review the research content related to entity disambiguation techniques.Firstly,the background of the domestic and international research on entity disambiguation is described,and the theories related to entity disambiguation such as named entity identification,candidate entity generation,and candidate entity ranking are comprehensively reviewed.Secondly,a detailed overview of the specific meaning of entity disambiguation and its research content is presented,and the characteristics of the research content of entity disambiguation are analyzed.Thirdly,the implementation methods of entity disambiguation techniques are classified into three categories and the data sets involved are summarized,and the difficulties in the field of entity disambiguation and the ways to improve the accuracy of entity disambiguation are discussed from four aspects,and the advantages and disadvantages of disambiguation methods and evaluation indexes are summarized,with the intention of providing new solutions for improving the effectiveness of entity disambiguation.Finally,the application and devel-opment prospects of entity disambiguation techniques are summarized.
entity disambiguationnamed entity identificationknowledge graphnatural language processingreview
万方数据
维普
