A Discourse-Based Network For A Natural Language System

ABSTRACT A discourse-based and cross-indexed semantic network has been devised as a knowledge structure for a natural language system. Within the network, word concepts are grouped in hierarchical fashion and linked to other related concepts; phrase structure sentence fragments derived from conversational input are stored as attributes under applicable word concept nodes and are used to provide multiple access points within the knowledge base. Additional features of the design exploit the inheritance properties of networks and the indirect linkages between word concepts. 1. INTRODUCTION The Informational Network for a Natural Talking (INFANT) system uses a language- and discourse-based semantic network to model a limited world of knowledge. It employs a minimal, 100-word vocabulary in a strict English phrase-structure environment to promote simplicity and to emphasize the importance of word and phrase relationships to the understanding of language; within the limits of the vocabulary, it provides a framework of word concept associations based on the usual linkages and inheritance properties of a semantic network. In addition, the INFANT system attempts to simulate the inherent uncertainty in human thought processes by assigning fuzzy association values to the words and phrases that make up its knowledge base. It has been designed primarily as a multiple-access information structure that contains the flexibility necessary to cope with a wide variety of simple English statements.Various network-oriented designs and applications have been developed which contribute directly to the sentence recognition techniques described later in the paper. Perhaps foremost among these are the 'spreading activation' approach of Quillian (1), whose work is generally regarded as the starting point for semantic network development and usage, the 'preference semantics1 model of Wilks (2,3), who used network-like templates of word formulas to analyze sentences. The concept of non-procedural (memory-based) learning is also of considerable importance to the methods adopted in this research effort. The work of Stanfill and Waltz (4) provides an informative background for amemory-based representation.approach to knowledge2. DESCRIPTION OF THE MODEL 2.1. Word conceptsThe INFANT system defines a work session environment in which the user-computer dialogue might involve a user's attempt to browse through a data file. Figure 1 shows part of the word concept network model upon which the system is built. General categories of words in the network are gradually decomposed into more specific categories as the 'TYPE-OF' links are traversed from top to bottom. For example, nouns are broken down into ABSTRACT and PHYSICAL objects; verbs are divided into ACTs and STATES, and the former is further subdivided into physical actions (DO) and mental actions (MENTAL). The structure is not truly hierarchical, for many of the words can be generalized to two or more categories, and therefore possess two or more parent nodes in the network. As examples, the inanimate but friendly COMPUTER is weakly 'animated1 (fuzzy value 2 on a scale of 0 to 9), and the concept of speaking (SAY) is considered partly an ACT of DOing (fuzzy 5) and partly a (MENTAL) STATE (fuzzy 7).In addition to the TYPE-OF links, a number of 'word attribute1 links are present in the VERB network of Figure 1. These describe, through arbitrarily assigned fuzzy association values, the inherent relationships between word concepts that might be implied during casual conversational usage. For example, the word concepts USE and HAVE claim each other as word attributes with association values of 8 and 6, respectively. Thus a subject's ability to 'use* an object implies with considerable certainty that the subject'has' the object, while the assertion that a subject 'has' something implies with lesser certainty that the subject is able to'use' it. (One has little use for a headache.) Admittedly, this informal method of cross-referencing is subject to substantial inaccuracy, but it forms the groundwork for the anticipated development of a facility for the instantiation of word- related implications throuqh a deductive analysis of input sentences.