Table understanding is the process of automatically recognizing,parsing,and applying tables that are widely available on the Internet and in vertical domains.Tables can be broadly classified into relational tables and non-relational tables.The former is similar to relational database tables,with a fixed structure easy for machine par-sing.The latter is usually more flexible in layout and syntax,with more obvious linguistic features,which is very challenging for computers to parse.Non-relational table understanding is one of the important emerging areas at the intersection of natural language and computer vision.With the popularity of deep learning technology in recent years,non-relational table understanding has been greatly developed in several directions,including recognition,se-mantic analysis,and application.This paper introduces the characteristics of non-relational tables,then systemati-cally introduces the recent developments in this field from the three research directions mentioned above.It also summarizes the public datasets related to non-relational tables,reveals the existing problems that need to be solved in non-relational table understanding and ends with possible future research directions.
table intelligencedeep learningmultimodal nature language processing
万方数据
维普
