Generation of natural language from information in a frame structure

摘要

Many expert systems and relational database systems store factual information in the form of attributes values of objects. Problems arise in transforming from that attribute database representation into English surface structure and vice versa. In this paper we consider only the generation process. In its interaction with the user, the expert system must generate questions, declarations, and uncertain declarations. Attributes such as COLOR, LENGTH, and ILLUMINATION can be referenced using the template: “〈attribute name〉 of 〈object〉” for both questions and declarations. However, many other attributes, such as RATTLES, in “What is RATTLES of the light bulb?”, and HAS_STREP_THROAT in, “HAS_STREP_ THROAT of Dan is true.” do not fit this template. We examined over 300 attributes from several knowledge bases and have grouped them into 16 classes. For each class there is one “question” template, one “declaration” template, and one “uncertain declaration” template for generating English surface structure. The internal databases identifiers (e.g. HAS_STREP_THROAT and DISEASE_35) must also be replaced by output synonyms. Classifying each attribute in combination with synonym translation remarkably improved the English surface structure that the system generated.