tailieunhanh - Báo cáo khoa học: "A KNOWLEDGE ENGINEERING APPROACH TO NATURAL LANGUAGE UNDERSTANDING"

A computer system is being developed to handle the acquisition, representation, and use of linguistic knowledge. The computer system is rule-based and utilizes a semantic network for knowledge storage and representation. In order to facilitate the interaction between user and system, input of linguistic knowledge and computer responses are in natural language. Knowledge of various types can be entered and utilized: syntactic and semantic; assertions and rules. | A KNOWLEDGE ENGINEERING APPROACH TO NATURAL LANGUAGE UNDERSTANDING Stuart c. Shapiro Jeannette G. Neal Department of Computer Science State University of New York at Buffalo Amherst New York 14226 ABSTRACT This paper describes the results of a preliminary study of a Knowledge Engineering approach to Natural Language Understanding. A computer system is being developed to handle the acquisition representation and use of linguistic knowledge. The computer system is rule-based and utilizes a semantic network for knowledge storage and representation. In order to facilitate the interaction between user and system input of linguistic knowledge and computer responses are in natural language. Knowledge of various types can be entered and utilized syntactic and semantic assertions and rules. The inference tracing facility is also being developed as a part of the rule-based system with output in natural language. A detailed example is presented to illustrate the current capabilities and features of the system. I INTRODUCTION This paper describes the results of a . preliminary study of a Knowledge Engineering KE approach to Natural Language Understanding NLU . The KE approach to an Artificial Intelligence task involves a close association with an expert in the task domain. This requires making it easy for the expert to add new knowledge to the computer system to understand what knowledge is in the system and to understand how the system is accomplishing the task so that needed changes and corrections are easy to recognize and to make. It should be noted that our task domain is NLU. That is the knowledge in the system is knowledge about NLU and the intended expert is an expert in NLU. The KE system we are using is the SNePS semantic network processing system 11 . This system es a semantic network system in which This work was supported in part by the National Science Foundation under Grants MCS80-06314 and SPI-8019895. all knowledge including rules is represented as .