In this paper we discuss various aspects of systematic or conventional polysemy and their formal treatment within an implemented constraint based approach to linguistic representation. We distinguish between two classes of systematic polysemy: constructional polysemy, where a single sense assigned to a lexical entry is contextually specialised, and sense extension, which predictably relates two or more senses. Formally the rst case is treated as instantiation of an underspecied lexical entry and the second by use of lexical rules. The problems of distinguishing between these two classes are discussed in detail. We illustrate how lexical rules can be used both to relate fully conventionalised senses and also applied productively to recognise novel usages and how this process can be controlled to account for semi-productivity by utilising probabilities. 1 Introduction Discussion of polysemy has been central to much recent work on lexical semantics. Most of the arguments for (or again...

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A Text-Based Intelligent System should provide more in-depth information about the contents of its corpus than does a standard information retrieval system, while at the same time avoiding the complexity and resource-consuming behavior of detailed text understanders. Instead of focusing on discovering documents that pertain to some topic of interest to the user, an approach is introduced based on the criterion of directionality (e.g., Is the agent in favor of, neutral, or opposed to the event?). A method is described for coercing sentence meanings into a metaphoric model such that the only semantic interpretation needed in order to determine the directionality of a sentence is done with respect to the model. This interpretation method is designed to be an integrated component of a hybrid information access system. 1 Introduction In the light of the increasing availability of computer-accessible full text, an important goal of a Text-Based Intelligent System is to provide a me...

This paper describes the results of an implemented computational model that cashes out the belief that metaphor interpretation is grounded in embodied primitives. The speci c task addressed is the interpretation of simple causal narratives in the domains of Politics and Economics. The stories are taken from newspaper articles in these domains. When presented with a preparsed version of these narratives as input, the system described is able to generate commonsense inferences consistent with the input.

In this paper we present TroFi (Trope Finder), a system for automatically classifying literal and nonliteral usages of verbs through nearly unsupervised word-sense disambiguation and clustering techniques. TroFi uses sentential context instead of selectional constraint violations or paths in semantic hierarchies. It also uses literal and nonliteral seed sets acquired and cleaned without human supervision in order to bootstrap learning. We adapt a word-sense disambiguation algorithm to our task and augment it with multiple seed set learners, a voting schema, and additional features like SuperTags and extrasentential context. Detailed experiments on hand-annotated data show that our enhanced algorithm outperforms the baseline by 24.4%. Using the TroFi algorithm, we also build the TroFi Example Base, an extensible resource of annotated literal/nonliteral examples which is freely available to the NLP research community. 1

. Sound engineering approaches to user interface design require the formalization of key interaction concepts, one of them being metaphor. Work on interface metaphors has, however, been largely non-formal so far. The few existing formal theories of metaphor have been developed in the context of natural language understanding, learning, or reasoning. We propose to formalize interface metaphors by algebraic specifications. This approach provides a comprehensive formalization for the essential aspects of metaphorical user interfaces. Specifically, metaphor domains are being formalized by algebras, metaphorical mappings by morphisms, and image-schemas by categories. The paper explains these concepts and the approach, using examples of spatial and spatializing metaphors. 1. Introduction Metaphor pervades communication. Metaphorical thought, action, and language are not only essential to interpersonal communication [Lakoff and Johnson 1980], but to human-computer communication as well. Sinc...

An unprecedented combination of simulative and metaphor based reasoning about beliefs is achieved in an AI system, ATT-Meta. Much mundane discourse about beliefs productively uses conceptual metaphors such as MIND AS CONTAINER and IDEAS AS INTERNAL UTTERANCES, and ATT-Meta's metaphor-based reasoning accordingly leads to crucial discourse comprehension decisions. ATT-Meta's non-metaphorical mode of belief reasoning includes simulative reasoning (SR). In ATT-Meta, metaphor-based reasoning can block and otherwise influence the course of SR. 1 INTRODUCTION In spoken and written discourse, mental states and processes are often described with the aid of commonsense models of mind. These models are largely metaphorical, and the metaphorical descriptions often convey information, about the quality of the mental states, that is important for understanding the discourse. In particular, the descriptions can clarify how agents can fail to draw even quite obvious conclusions from their beliefs. A...

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. ...

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 ...

We present and evaluate an approach to the reasoning needed to handle a broad class of metaphorical ut-terances, and a computer program (ATT-Meta) partially implementing and further specifying that approach. The approach emanates from artificial intelligence but is offered also for consideration by cognitive scien-tists generally. The utterances of interest are ones that (a) rest on conceptual metaphors that are familiar to the understander but (b) transcend the mappings in the conceptual metaphors by using concepts not han-dled by the mappings. Our approach advocates possibly-extensive inferencing in the terms of the source (vehicle) domains of the conceptual metaphors, while avoiding as far as possible the extension of the map-pings to deal with the concepts they do not handle. The general approach is similar in flavor to those of a small number of other metaphor researchers, but we provide a more extensive analysis, additional principles and a more thorough-going implementation. The approach contains a number of “view-neutral mapping adjuncts, ” which are default mapping principles that enable important source-domain aspects to be mapped to the target domain, independently of which specific metaphorical views are in play. Many discussions of metaphor appear to assume that such mapping actions occur, but rarely address them systematically and explicitly. In addition, in the approach, a conceptual metaphor can consist not only of a between-domain mapping but also of special, ancillary assumptions that serve to enrich the source domain with specific de-tails needed by the metaphors. The implemented system supports ancillary assumptions but currently only has a preliminary handling of view-neutral mapping adjuncts. 2 1