We derive the rhetorical structures of texts by means of two new, surface-form-based algorithms: one that identifies discourse usages of cue phrases and breaks sen- tences into clauses, and one that produces valid rhetorical structure trees for unre- stricted natural language texts. The algo- rithms use information that was derived from a corpus analysis of cue phrases.

I wish honorable gentlemen would have the fairness to give the entire context of what I did say, and not pick out detached words. (Cobden, Speeches 46, 1849, quoted intheOED) The importance of contextual reasoning is emphasized by various researchers in AI.

This paper shows how to construct semantic representations from the derivations produced by a wide-coverage CCG parser. Unlike the dependency structures returned by the parser itself, these can be used directly for semantic interpretation. We demonstrate that well-formed semantic representations can be produced for over 97 % of the sentences in unseen WSJ text. We believe this is a major step towards widecoverage semantic interpretation, one of the key objectives of the field of NLP. 1

this paper gives a comprehensive picture of a complex, yet not explicitly spelled-out theory of discourse coherence, the centering model (Grosz, Joshi, and Weinstein, 1983, 1995) marked a major step in clarifying the relationship between attentional states and (local) discourse segment structure. More precisely, the centering model accounts for the interactions between local coherence and preferential choices of referring expressions. It relates differences in coherence (in part) to varying demands on inferences as required by different types of referring expressions, given a particular attentional state of the hearer in a discourse setting (Grosz, Joshi, and Weinstein 1995, 204-205). The claim is made then that the lower the inference load put on the hearer, the more coherent the underlying discourse appears. The centering model as formulated by Grosz, Joshi, and Weinstein (1995) refines the structure of "centers" of discourse, which are conceived as the representational device for the attentional state at the local level of discourse. They distinguish two basic types of centers, which can be assigned to each utterance Ui--a single backward- looking center, Cb(Ui), and a partially ordered set of discourse entities, the forward- looking centers, Cf(Ui). The ordering on Cf is relevant for determining the Cb. It can be viewed as a salience ranking that reflects the assumption that the higher the ranking of a discourse entity in Cf, the more likely it will be mentioned again in the immediately following utterance. Thus, given an adequate ordering of the discourse entities in Cf, the costs of computations necessary to establish local coherence are minimized

this paper, we'll consider three examples where this occurs: logical metonymy (e.g., enjoy the book means enjoy reading the book), adjectives (e.g., the interpretation of fast in fast car, fast motorway, fast typist etc.), and noun-verb agreement. We'll argue for a new version of default inheritance, which allows default results of lexical generalisations to persist as default beyond the lexicon. We'll show that this persistence can be exploited by the pragmatic component, to reason about when generalisations encoded in the lexicon survive in a discourse context. We'll represent the link between the lexicon and pragmatics via two axioms. These will predict the pragmatic exceptions to lexical generalisations that arise in a discourse context. We thereby explain how words are interpreted in discourse, in a way that neither the lexicon nor pragmatics could achieve on their own.

The DIPPER architecture is a collection of software agents for prototyping spoken dialogue systems. Implemented on top of the Open Agent Architecture (OAA), it comprises agents for speech input and output, dialogue management, and further supporting agents. We define a formal syntax and semantics for the DIPPER information state update language. The language is independent...

Abstract. We present the syntax and semantics for a multi-agent dialogue game protocol which permits argument over proposals for action. The protocol, called the PARMA Protocol, embodies an earlier theory by the authors of persuasion over action which enables participants to rationally propose, attack, and defend, an action or course of actions (or inaction). We present an outline of both an axiomatic and a denotational semantics, and discuss an implementation of the protocol for two human agents. 1

Wide-coverage and robust NLP techniques always seemed to go hand in hand with shallow analyses. This was certainly true a couple of years ago,

Introduction Pragmatics is commo,nly understood to be concerned with studying the relations between linguistic phenomena and properties of the context of use. The understanding of these relations is important in many areas of theoretical and applied research, from grammatical analysis to sociolinguistic field studies. One area where the importance of these relations has become particularly clear is the design of language understanding systems. Such systems are extremely limited, brittle, and unpractical if they do not have powerful ways to make use of contextual information in computing the meanings of utterances. The question of how this can be achieved in an effective and principled way forms one of the major obstacles in building such systems. Computational pragmatics, the study of how contextual information can be effectively brought to bear in language understanding and production processes, hopes to contribute to removing this obstacle. One way in which contextual infor

This article presents a new semanticbased transfer approach developed and applied within the Verbmobil Machine Translation project. We give an overview of the declarative transfer fo,'malism to- gether with its procedural realization. Our approach is discussed and compared with several other approaches f,'om the MT literature. The results presented in this article have been implemented and integrated into the Verbmobil system.