U.S.A. Abstract: This paper presents a discussion of means of describing the discourse and its components which makes speech act interpretation and anaphora disambiguation possible with minimal search of the knowledge in the database. A portion of this paper will consider how a frames representation of sentences and common sense knowledge provides a mechanism for representing the postulated discourse components. Finally some discussion of the use of the discourse model and of frames in a discourse understanding program for a personal assistant will be presented. Introduction The person who communicates with a personal assistant, whether human or machine, wants to request some action of the assistant via sentences in English. Generally, a single sentence is insufficient to capture all the information that is to be given as well as an unnatural way to make a request. However, as several example dialogues below will show, the human user does not tightly relate the sentences s/he speaks about a particular subject. It is instead the job of the hearer to interpret how the incoming sentence is related to the previous discourse. Each sentence or clause of a discourse that makes some demand upon the hearer must be interpreted for the kind of demand being made. These demands are generally referred to as speech acts {1}. However, the speech acts are not just strings of individual requests. They have a connecting pattern which the hearer must extract as the discourse goes on. Thus the hearer's task is twofold: to interpret the speech act in a clause and to relate that speech act to the overall discourse. In this paper this two-pronged task will be referred to as speech act interpretation. Closely associated with speech act interpretation is the process of understanding what the various noun and pronoun phrases of the incoming sentence refer to. Speakers denote previously mentioned objects in a variety of ways with apparent ambiguity in the choice of referents.

Rather than using a single uniform representation, the program uses a number of different representations, each of which is specialized for a particular task, e.g., language syntax, language semantics, representing objects and their attributes and relationships, representing objects as canonical objects used in physics, modelling geometry, and solving equations. Many of these representations are based on the notion of frames [Minsky 75]. The use of specialized representations simplifies many of the processes which must be performed by the program; however, it requires that the program be able to translate between the various representations when necessary. Procedural knowledge is required to convert one representation into another, since it frequently happens that information which is essential in the target representation is unspecified or is specified only implicitly in the source representation; inferences are required to fill in such information. Specialized representations allow procedures to be attached to particular types of representations, both to convert them to other types and to solve problems which are associated with the specialized area. In this paper, we discuss the ways in which these techniques are used to coordinate the many kinds of knowledge which are necessary for solving physics problems.

This paper describes a computer system for understanding English. The system answers questions, executes commands, and accepts information in an interactive English dialog. It is based on the belief that in modeling language understanding, we must deal in an integrated way with all of the aspects of language—syntax, semantics, and inference. The system contains a parser, a recognition grammar of English, programs for semantic analysis, and a general problem solving system. We assume that a computer cannot deal reasonably with language unless it can understand the subject it is discussing. Therefore, the program is given a detailed model of a particular domain.

Lexical ambiguity can be syntactic if it involves more than one grammatical category for a single word, or semantic if more than one meaning can be associated with a word. In this article we discuss the application of a Bayesian-network model in the resolution of lexical ambiguities of both types. The network we propose comprises a parsing subnetwork, which can be constructed automatically for any context-free grammar, and a subnetwork for semantic analysis, which, in the spirit of Fillmore's (1968) case grammars, seeks to fulfill the required cases of all candidates for verb of the sentence. Solving for the highest joint probability of the variables conditioned upon the evidences to the network yields the most likely candidate with its meaning, along with its cases and respective meanings. This is achieved by fixing the values of all evidence nodes concurrently, and then performing a stochastic simulation in which the remaining nodes are updated probabilistically with a high degree of parallelism.

Syntax, Preference and Right Attachment Yorick Wilks, Xiuming Huang & Dan Fass Computing Research Laboratory New Mexico State University Las Cruces, NM, USA 88003 ABSTRACT The paper claims that the right attachment rules for phrases originally suggested by Frazier and Fodor are wrong, and that none of the subsequent patchings of the rules by syntactic methods have improved the situation. For each rule there are perfectly straightforward and indefinitely large classes of simple counterexamples. We then examine suggestions by Ford et a!., Schubert and Hirst which are quasi-semantic in nature and which we consider ingenious but unsatisfactory. We offer a straightforward solution within the framework of preference semantics, and argue that the principal issue is not the type and nature of information required to get appropriate phrase attachments, but the issue of where to store the information and with what processes to apply it. We present a prolog implementation of a best first algorithm covering the data and contrast it with closely related ones, all of which are based on the preferences of nouns and prepositions, as well as verbs.

The program has two modes: PARAPHRASE and INFERENCE. In PARAPHRASE mode up to 150 semantic paraphrases can be generated from an input sentence by reading the conceptual representation underlying that sentence using different words and concept combinatione.

Publication 29, Institute of Linguistics, University of Stockholm.

We present a novel statistical approach to semantic parsing, WASP, for constructing a complete, formal meaning representation of a sentence. A semantic parser is learned given a set of sentences annotated with their correct meaning representations. The main innovation of WASP is its use of state-of-the-art statistical machine translation techniques. A word alignment model is used for lexical acquisition, and the parsing model itself can be seen as a syntax-based translation model. We show that WASP performs favorably in terms of both accuracy and coverage compared to existing learning methods requiring similar amount of supervision, and shows better robustness to variations in task complexity and word order.

The aim of this paper is to present in a rigorous way the syntax and semantics of a certain fragment of a certain dialect of English. Patrick Suppes claims, in a paper prepared for the present workshop [the 1970 Stanford Workshop on Grammar and Semantics], that at the present time the semantics of natural languages are less satisfactorily formulated than the grammars ¼ [and] a complete grammar for any significant fragment of natural language is yet to be written.'' This claim would of course be accurate if restricted in its application to the attempts emanating from the Massachusetts Institute of Technology, but fails to take into account the syntactic and semantic treatments proposed in Montague (1970a, b). Thus the present paper cannot claim to present the first complete syntax (or grammar, in Suppes' terminology) and semantics for a significant fragment of natural language; and it is perhaps not inappropriate to sketch relations between the earlier proposals and the one given below. Montague (1970b) contains a general theory of languages, their interpretations, and the inducing of interpretations by translation.

In Rustin, 111-154.