Knowledge Structures for Natural Language Generation

Knowledge Structures for Natural Language Generatlon ~ Paul S. Jacobs Knowledge-Based Systems Branch General Electric Corporate Research and Development Schenectady, NY 12301 USA Abstract The development of natural language interfaces to Artifi- cial Intelligence systems is dependent on the representation of knowledge. A major impediment to building such systems has been the difficulty in adding sufficient linguistic and concep- tual knowledge to extend and adapt their capabilities. This difficulty has been apparent in systems which perform the task of language production, i. e. the generation of natural language output to satisfy the communicative requirements of a system. The Ace framework applies knowledge representation fun- damentals to the task of encoding knowledge about language. Within this framework, linguistic and conceptual knowledge are organized into hierarchies, and structured associations are used to join knowledge structures that are metaphorically or referentially related. These structured associations per- mit specialized linguistic knowledge to derive partially from more abstract knowledge, facilitating the use of abstractions in generating specialized phrases. Thls organization, used by a generator called KING (Knowledge INtensive Generator), promotes the extenslbility and adaptability of the generation system. 1 Introduction The task of natural language generation is that of producing linguistic output to satisfy the communicative requirements of a computer system. The principal limitation of existing programs which perform this function is that they fail to realize a sufficiently broad range of requirements to demon- strate a convincing linguistic capability. This drawback seems founded in aspects of the systems which hinder the develop- ment of a large base of knowledge about language. A great deal of knowledge is required to produce any given utterance, yet much of this knowledge cannot easily be exploited across a range of utterances. Partial success in generation systems is often achieved by applying linguistic knowledge to particular domains. Exem- plary of this success are text generation programs such as PROTEUS [6], and Ann [15,14]; as well as generation com- ponents of on-line systems; for example, in HAM-ANS [5], UC [9,11[, and VIE-LANG [4 I. These systems, while em- bodying a variety of generation techniques, serve to illustrate the importance of the command of specialized constructs and the ability to utilize specialized knowledge in generation. A close examination of the knowledge used in such programs, however, reveals that a great deal of linguistic information seems to be encoded redundantly, thus impeding the use of generalizations in "scaling up ~ the systems. The UNIX 2 Consultant system [22] is a program which answers questions from naive users about the UNIX operating system. Scaling up the user interface required a generator which could produce responses such as the following: 1. 'Chmod' can be used to give you write permission. 2. You don't have write permission on the directory. 3. You can't get write permission on the directory. 4. You need ethernet access. 'This paper is based on the thesis research conducted while the au- thor was at the University of California, Berkeley. The research was supported in part by the Office of Naval …

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