We present a representation of events and action based on interval temporal logic that is significantly more expressive and more natural than most previous AI approaches. The representation is motivated by work in natural language semantics and discourse, temporal logic, and AI planning and plan recognition. The formal basis of the representation is presented in detail, from the axiomatization of time periods to the relationship between actions and events and their effects. The power of the representation is illustrated by applying it to the axiomatization and solution of several standard problems from the AI literature on action and change. An approach to the frame problem based on explanation closure is shown to be both powerful and natural when combined with our representational framework. We also discuss features of the logic that are beyond the scope of many traditional representations, and describe our approach to difficult problems such as external events and simultaneous action...

This paper presents a formal account of how to determine the discourse relations between sentences in a text, and the relations between the events they describe. The distinct natural interpretations of texts with similar syntax are explained in terms of defeasible rules. These characterise the effects of causal knowledge and knowledge of language use on interpretation. Patterns of defeasible entailment that are supported by the logic in which the theory is expressed are shown to underly temporal interpretation. 1 The Problem of Temporal Relations An essential part of text interpretation involves calculating the relations between the events described. But sentential syntax and compositional semantics alone don't provide the basis for doing this. The sentences in (1) and (2) have the same syntax, and so using compositional semantics one would predict that the events stand in similar temporal relations. (1) Max stood up. John greeted him. (2) Max fell. John pushed him. But in (1...

This paper presents a formal account of the temporal interpretation of text. The distinct nat- ural interpretations of texts with similar syntax are explained in terms of defeasible rules charac- tcrising causal laws and Gricean-style pragmatic maxims. Intuitively compelling patterns of defea. sible entaJlment that are supported by the logic in which the theory is expressed are shown to underly temporal interpretation.

this paper, I consider a range of English expressions and show that their context-dependency can be characterized in terms of two properties: 1. They specify entities in an evolving model of the discourse that the listener is constructing; 2. The particular entity specified depends on another entity in that part of the evolving "discourse model" that the listener is currently attending to

This article reviews research on the use of situation models in lnguage comprehension and memory retrieval over the past 15 years. Situation models are integrated mental representations of a described state of affairs. Significant progress has been made in the scientific understanding of how situation models are involved in language comprehension and memory retrieval. Much of this research focuses on establishing the existence of situation models, often by using tasks that assess one dimension of a situation model. However, the authors argue that the time has now come for researchers to begin to take the multidimensionality of situation models seriously. The authors offer a theoretical framework and some methodological observations that may help researchers to tackle this issue.

This paper proposes a solution to the computational task of extracting temporal information from simple declarative sentences based on separating temporal analysis into distinct tasks, each of which has access to a selected portion of the temporal input. The ultimate goal is to represent temporal information as explicitly as possible at each stage of analysis in order to provide the appropriate information for the next stage. Because the representations are constructed incrementally, it is important that they should be explicit about what has been derived so far, yet sufficiently noncommittal to avoid conflicting with subsequent processing

We present a semantic and pragmatic account of the anaphoric properties of past and perfect that improves on previous work by integrating discourse structure, aspectual type, surface structure and commonsense knowledge. A novel aspect of our account is that we distinguish between two kinds of temporal intervals in the interpretation of temporal operators -- discourse reference intervals and event intervals. This distinction makes it possible to develop an analogy between centering and temporal centering, which operates on discourse reference intervals. Our temporal property-sharing principle is a defeasible inference rule on the logical form. Along with lexical and causal reasoning, it plays a role in incrementally resolving underspecified aspects of the event structure representation of an utterance against the current context.

This paper consists of two parts. The first part discusses commonsense knowledge about events as manifested in language. Three kinds of knowledge are identified: compositional, durational, and aspectual. Compositional knowledge concerns internal structuring of events into preparatory, initial, main (the body), final, and resulting stages. Durational knowledge concerns durational relations between events and stages of the same event. Durational knowledge can be expressed as qualitative dependencies among the parameters of the event and as its time scale. The notion of time scale is introduced and related to shared cyclical events (time units). In discussing

We focus on the problem of building large repositories of lexical conceptual structure (LCS) representations for verbs in multiple languages. One of the main results of this work is the definition of a relation between broad semantic classes and LCS meaning components. Our acquisition program---LEXICALL---takes, as input, the result of previous work on verb classification and thematic grid tagging, and outputs LCS representations for different languages. These representations have been ported into English, Arabic and Spanish lexicons, each containing approximately 9000 verbs. We are currently using these lexicons in an operational foreign language tutoring and machine translation. 1 Introduction A wide range of new capabilities in NLP applications such as foreign language tutoring (FLT) has been made possible by recent advances in lexical semantics (Carrier and Randall, 1993; Dowty, 1991; Fillmore, 1968; Foley and Van Valin, 1984; Grimshaw, 1990; Gruber, 1965; Hale and Keyser, 1993; ...

In this thesis, we present analyses and algorithms for resolving a variety of cohesive phenomena in natural language, including VP-ellipsis, gapping, event reference, tense, and pronominal reference. Past work has attempted to explain the complicated behavior of these expressions with theories that operate within a single module of language processing. We argue that such approaches cannot be maintained; in particular, the data we present strongly suggest that the nature of the coherence relation operative between clauses needs to be taken into account. We provide a theory of coherence relations and the discourse inference processes that underly their recognition. We utilize this theory to break the deadlock between syntactic and semantic approaches to resolving VP-ellipsis. We show that the data exhibits a pattern with respect to our categorization of coherence relations, and present an account which predicts this pattern. We extend our analysis to gapping and event reference, and sho...