The workshop that inspired this book was the high point of a collaborative research project entitled ‘Incorporation of Semantics into Computational Linguistics’, supported by the EEC through the COST13 programme. The workshop was also the first official academic event to be organized by IDSIA, a newly inaugurated institute of the Dalle Molle Foundation situated in Lugano, Switzerland devoted to artificial intelligence research.

A few words about the project may help to place the contents of the book in its proper context. The underlying theme was that although human language is studied from the standpoint of many different disciplines, there is rather less constructive interaction between the disciplines than might be hoped for, and that for such interaction to take place, a common framework of some kind must be established. Computational linguistics (CL) and artificial intelligence (AI) were singled out as particular instances of such disciplines: each has focused on rather different aspects of the relationship between language and computation.

Thus, what we now call CL grew out of attempts to apply the concepts of computer science to generative linguistics (e.g. Zwicky and his colleagues [255], Petrick [181]). Given these historical origins, CL unwittingly inherited some of the goals for linguistic theory that we normally associate with Chomskyan linguistics. One such goal, for example, is to finitely describe linguistic competence, that is, the knowledge that underlies the human capacity for language by means of a set of universal principles that characterize the class of possible languages, and a set of descriptions for particular languages.

INTRODUCTION

This paper is written from the point of view of one who works in artificial intelligence (AI): the attempt to reproduce interesting and distinctive aspects of human behaviour with a computer, which, in my own case, means an interest in human language use.

There may seem little of immediate relevance to cognition or epistemology in that activity. And yet it hardly needs demonstration that AI, as an aspiration and in practice, has always been of interest to philosophers, even to those who may not accept the view that AI is, essentially, the pursuit of metaphysical goals by non-traditional means.

As to cognition in particular, it is also a commonplace nowadays, and at the basis of cognitive science, that the structures underlying AI programs are a guide to psychologists in their empirical investigations of cognition. That does not mean that AI researchers are in the business of cognition, nor that there is any direct inference from how a machine does a task, say translating a sentence from English to Chinese, to how a human does it. It is, however, suggestive, and may be the best intellectual model we currently have of how the task is done. So far, so well known and much discussed in the various literatures that make up cognitive science.

INTRODUCTION

Large practical computational-linguistic applications, such as machine translation systems, require a large number of knowledge and processing modules to be put together in a single architecture and control environment. Comprehensive practical systems must have knowledge about speech situations, goal-directed communicative actions, rules of semantic and pragmatic inference over symbolic representations of discourse meanings and knowledge of syntactic and phonological/graphological properties of particular languages. Heuristic methods, extensive descriptive work on building world models, lexicons and grammars as well as a sound computational architecture are crucial to the success of this paradigm. In this paper we discuss some paradigmatic issues in building computer programs that understand and generate natural language. We then illustrate some of the foundations of our approach to practical computational linguistics by describing a language for representing text meaning and an approach to developing an ontological model of an intelligent agent. This approach has been tested in the dionysus project at Carnegie Mellon University which involved designing and implementing a natural language understanding and generation system.

SEMANTICS AND APPLICATIONS

Natural language processing projects at the Center for Machine Translation of Carnegie Mellon University are geared toward designing and building large computational applications involving most crucial strata of language phenomena (syntactic, semantic, pragmatic, rhetorical, etc.) as well as major types of processing (morphological and syntactic parsing, semantic interpretation, text planning and generation, etc.). Our central application is machine translation which is in a sense the paradigmatic application of computational linguistics.

INTRODUCTION

This paper stands in marked contrast to many of the others in this volume in that it is intended to be entirely tutorial in nature, presupposing little on the part of the reader but a user's knowledge of English, a modicum of good will, and the desire to learn something about the notion of unification as it has come to be used in theoretical and computational linguistics. Most of the ideas I shall be talking about were first introduced to the study of ordinary language by computational linguists and their adoption by a notable, if by no means representative, subset of theoretical linguists represents an important milestone in our field, for it is the first occasion on which the computationalists have had an important impact on linguistic theory. Before going further, there may be some value in pursuing briefly why this rapprochement between these two branches has taken so long to come about. After all, the flow of information in the other direction – from theoretician to computationalist – has continued steadily from the start.

PRODUCTIVITY

The aspects of ordinary language that have proved most fascinating to its students all have something to do with its productivity, that is, with the fact that there appears to be no limit to the different utterances that can be made and understood in any of the languages of the world. Certainly, speakers can make and understand utterances that they have never made or understood before, and it is presumably largely in this fact that the immense power and flexibility of human language resides.

INTRODUCTION

The integration of syntactic and semantic processing has prompted a number of different architectures for natural language systems, such as rule-by-rule interpretation [221], semantic grammars [22] and cascaded ATNs [253]. The relationship between syntax and semantics has also been of central concern in theoretical linguistics, particularly following Richard Montague's work. Also, with the recent rapprochement between theoretical and computational linguistics, Montague's interpretation scheme has made its way into natural language systems. Variations on Montague's interpretation scheme have been adopted and implemented in several syntactic theories with a significant following in computational linguistic circles. The first steps in this direction were taken by Hobbs and Rosenschein [89]. A parser for LFG was augmented with a Montagovian semantics by Halvorsen [76]. GPSG has been similarly augmented by Gawron et al. [65], and Schubert and Pelletier [206] followed with a compositional interpretation scheme using a first order logic rather than Montague's computationally intractable higher-order intensional logic.

In parallel with these developments in the syntax/semantics interface, unification-based mechanisms for linguistic description had significant impact both on syntactic theory and syntactic description. But the Montagovian view of compositionality in semantics and the architectures for configuring the syntax/semantics interface were slower to achieve a similar revaluation in view of unification and the new possibilities for composition which it brought.

INTRODUCTION

This chapter concerns experiments with Situation Schemata as a linguistic representation language, in the first instance for Machine Translation purposes but not exclusively so. The work reported is primarily concerned with the adaptation of the Situation Schemata language presented by Fenstad et al. [56] (but see also Fenstad et al. in this volume) to an implementation within grammars having interestingly broad linguistic coverage. We shall also consider the computational tools required for such an implementation and our underlying motivation. The chapter is therefore not solely concerned with the form of the representation language and its relation to any eventual interpretation language, but also with practical and methodological issues associated with the implementation of current theories in Computational Linguistics. A key theme is the interplay between linguistic information, representation and appropriate mechanisms for abstraction.

The rest of the chapter is divided into three further sections. Section 7.2 suggests that the problem of translation can shed light on a range of representational issues on the syntax/semantics border; it contains a brief introduction to Situation Semantics and the original version of Situation Schemata. Section 7.3 outlines the design of a computational toolkit for experimenting with unification-based descriptions, concentrating on details of the grammar formalism and its abstraction mechanisms. Section 7.4 considers the form of Situation Schemata that has been implemented within a grammar of German and some related representational issues that arise out of this work.

INTRODUCTION

Computational semantics lies at the intersection of three disciplines: linguistics, logic and computer science. A natural language system should provide a coherent framework for relating linguistic form and semantic content, and the relationship must be algorithmic.

There have been several important pairwise interactions, as detailed below, between linguistics and computer science, linguistics and logic, and logic and computer science. The point of computational semantics is the insistence on relating all three simultaneously. This is necessary from a cognitive as well as a knowledge engineering point of view.

LINGUISTICS AND COMPUTER SCIENCE

N. Chomsky's Syntactic Structures [34] signalled a renewal of theoretical linguistics, which for some of its theoretical tools drew upon automata and formal language theory. A link was soon established with the emerging computer science, leading to a vigorous field of computational linguistics, focusing on questions of linguistic form, i.e. syntax and morphology. This has proved to be of lasting value for the study of both natural and programming languages.

LINGUISTICS AND LOGIC

Language is more than linguistic form. R. Montague, in a series of papers starting from 1967 (see Thomason [219]), showed how to use the insights from logical semantics to ‘tame’ the meaning component of a natural language. Montague's tool was the model theory of higher order intensional logic. And he convincingly demonstrated how the use of this model theory could explain a wide range of linguistic phenomena.