Novel Framework for Future Natural Language Processing From a Database Perspective

  Most research and applications on natural language still concentrate on its superficial features and structures. However, natural language is essentially a way of encoding information and knowledge. Thus, the focus should be on what is encoded and how it is encoded. In line with this, we suggest a database-based approach for natural language processing that emulates the encoding of information and knowledge to build models. Based on these models, 1) generating sentences becomes akin to reading data from the models (or databases) and encoding it following some rules; 2) understanding sentences involves decoding rules and a series of boolean operations on the databases; 3) learning can be accomplished by writing on the databases. Our method closely mirrors how the human brain processes information, offering excellent interpretability and expandability.