The problems of access -- retrieving linguistic structure from some mental grammar -- and disambiguation -- choosing among these structures to correctly parse ambiguous linguistic input -- are fundamental to language understanding. The literature abounds with psychological results on lexical access, the access of idioms, syntactic rule access, parsing preferences, syntactic disambiguation, and the processing of garden-path sentences. Unfortunately, it has been difficult to combine models which account for these results to build a general, uniform model of access and disambiguation at the lexical, idiomatic, and syntactic levels. For example psycholinguistic theories of lexical access and idiom access and parsing theories of syntactic rule access have almost no commonality in methodology or coverage of psycholinguistic data. This paper presents a single probabilistic algorithm which models both the access and disambiguation of linguistic knowledge. The algorithm is based on a parallel parser which ranks constructions for access, and interpretations for disambiguation, by their conditional probability. Low-ranked constructions and interpretations are pruned through beam-search; this pruning accounts, among other things, for the garden-path effect. I show that this motivated probabilistic treatment accounts for a wide variety of psycholinguistic results, arguing for a more uniform representation of linguistic knowledge and for the use of probabilisticallyenriched grammars and interpreters as models of human knowledge of and processing of language.

Metaphor is a conventional and ordinary part of language. An approach to metaphor, based on the explicit representation of knowledge about metaphors, has been developed. This approach asserts that the interpretation of conventional metaphoric language should proceed through the direct application of specific knowledge about the metaphors in the language. midas (Metaphor Interpretation, Denotation, and Acquisition System) is a computer program that has been developed based upon this approach. midas can be used to represent knowledge about conventional metaphors, interpret metaphoric language by applying this knowledge, and dynamically learn new metaphors as they are encountered during normal processing. 2 1 Conventional Metaphor Consider the problem of understanding the conventional metaphoric language in the following examples. (1) How can I kill a process? (2) How can I get into Lisp? (3) You can enter Emacs by typing "emacs" to the shell. (4) Nili gave Marc her cold. ...

Existing plan-based theories of speech act interpretation do not account for the conventional aspect of speech acts. We use patterns of linguistic features (e.g. mood, verb form, sentence adverbials, thematic roles) to suggest a range of speech act interpretations for the utterance. These are filtered using plan-based conversational implicatures to eliminate inappropriate ones. Extended plan reasoning is available but not neces.'y for familiar forms. Taking speech act ambiguity seriously, with these two constraints, explains how "Can you pass the salt?" is a ical indirect request while "Are you typ . able to pass the salt?" m not.

We define within-vehicle and within-tenor reasoning to be reasoning that is done on-the-fly within the vehicle domain or tenor domain, respectively, of a conceptual metaphor, during the comprehension of utterances that manifest the metaphor. The main claim of this paper is that, at least in Artificial Intelligence systems for understanding metaphorical discourse, within-vehicle reasoning is often beneficial. Indeed, in several important respects it is to be preferred over within-tenor reasoning that would achieve the same overall effect; and in any case for certain types of metaphorical sentence there is useful within-vehicle reasoning that can be done but for which there is no feasible within-tenor parallel. Although some work on metaphor involves within-vehicle reasoning, its benefits do not appear to have been explained and argued. The examples in the present paper focus on metaphors for the particular domain of mental states and processes; also, the examples involve only metaphors ...

The article discusses claims that conceptual structure is in some part metaphorical, as identified by verbal metaphors like LOVE IS A JOURNEY. Two main interpretations of this view are discussed. In the first, a target domain is not explicitly represented but is instead understood through reference to a different domain. For example, rather than a detailed concept of love per se, one could make reference to the concept of a journey. In the second interpretation, there is a separate representation of love, but the content of that representation is influenced by the metaphor such that the love concept takes on the same structure as the journey concept. It is argued that the first interpretation is not fully coherent. The second interpretation is a possible theory of mental representation, but the article raises a number of empirical and theoretical problems for it. It is concluded that many of the data cited as evidence for metaphoric representations can be accounted for by structural similarity between domains. 1.

In this paper we propose algorithms to automatically classify sentences into metaphoric or normal usages. Our algorithms only need the WordNet and bigram counts, and does not require training. We present empirical results on a test set derived from the Master Metaphor List. We also discuss issues that make classification of metaphors a tough problem in general. 1

This article describes our attempts to shed light on the relationship between laboratorybased research on the effects of context on metaphor and the nature of metaphor as it occurs in naturally occurring text. The specific hypothesis underlying this work is that the facilitation and inhibition effects observed in laboratory subjects reflect the patterns of co-occurrence of various kinds of contexts with metaphoric language in the environment. We take a three part approach to exploring this hypothesis: a rational analysis of the notion of a context effect, an empirical corpus-based effort to fill out that rational analysis, and a reconsideration of the pertinent psycholinguistic results with respect to that analysis. Finally, we present a proposal for a mechanistic model that is in accord with the results of our analysis. 2 1 Introduction A wide variety of results from psycholinguistic research over the last several decades have shown that context has a strong effect on the ...

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We describe an approach to deriving efficient rules for interpreting the intended meaning of indirect speech acts. We have constructed a system called sally that starts with a few, very general principles for understanding the intention of the speaker of an utterance. After inferring the intended meaning of a particular utterance, sally creates a specialized rule to understand directly similar utterances in the future. Introduction: Indirect Speech Acts Responding appropriately to a question often requires the listener to understand the intention of the speaker. For example, consider the following simple question: Q: Do you have a match? Taken literally, this question is a request for information. However, in most contexts, this question should be interpreted as a request for the listener to give the speaker a match. This is a kind of speech act (Austin, 1962) called an indirect speech act (Searle, 1975), in which the intent of the speaker differs from the direct, literal meaning of...

this paper. c 2002 Psychology Press Ltd http://www.tandf.co.uk/journals/pp/01690965.html DOI: 10.1080/01690960042000166 LANGUAGE AND COGNITIVE PROCESSES, 2002, 17 (2), 97--123 expressions can have at least two different interpretations (Donnellan, 1966, 1978; Searle, 1979). For example, a sentence containing a de#nite description (as in 1a; adapted from Donnellan, 1966, see Roberts, 1993): 1a. The murderer of Smith is insane