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this paper, contains literal, metonymic, metaphorical, and anomalous semantic relations. The assertion-based class of relations are described in greater length in Fass (1989a)

this paper, between traditional IR and the newer IE , is not totally clear everywhere but can itself become a question of degree. Suppose parsing systems that produce syntactic and logical representations were so good, as some now believe, that they could process huge corpora in an acceptably short time. One can then think of the traditional task of computer question answering in two quite different ways. The old way was to translate a question into a formalised language like SQL and use it to retrieve information from a database- as in "Tell me all the IBM executives over 40 earning under $50K a year". But with a full parser of large corpora one could now imagine transforming ing the query to form an IE template and searching the WHOLE TEXT (not a data base) for all examples of such employees---both methods should produce exactly the same result starting from different information sources --- a text versus a formalised database. What we have called an IE template can now be seen as a kind of frozen query that one can reuse many times on a corpus and is therefore only important when one wants stereotypical, repetitive, information back rather than the answer to one-off questions. "Tell me the height of Everest?", as a question addressed to a formalised text corpus is then neither a IR nor IE but a perfectly reasonable single request for an answer. "Tell me about fungi", addressed to a text corpus with an IR system, will produce a set of relevant documents but no particular answer. Tell me what films my favourite movie critics likes, addressed to the right text corpus , is undoubtedly IE as we saw, and will produce an answer also. The needs and the resources available determine the techniques that are relevant, and those in turn determine what it is to answer a questio...

The frequent and conventional use of non--literal language has been a major stumbling block for natural language processing systems since the early machine translation efforts. Metaphor, metonymy and indirect speech acts are among the most troublesome phenomena. Recent computational efforts addressing these problems have taken an approach that emphasizes the use of systematic knowledge about non--literal language conventions. We are currently engaged in an effort to supply this knowledge in the case of conventional metaphor. We are constructing MetaBank: an empirically derived and theoretically motivated knowledge--base of English metaphorical conventions. This article describes our three--part approach to the construction of MetaBank: the collection of on--line textual resources and databases of linguistic generalizations, the development of a methodology for analyzing these resources, and the construction of a knowledge--base based on the preceding analyses. 2 1 Introduc...

Introduction This proper describes a computational approach to metonymy and.metaphor that distinguishes between them, literalhess, and anomaly. Tile approach lends support to the views of Lakoff and Johnson (1980) that metonymy and nmtaphor are quite differeut phenomeua, that metonymy is a means by which one entity stands for another, whereas metaphor is a way iu which one entity is vietved as miother. The three main features of file computational approach are that: (a) literahtess, metaphor, and anomaly share common features and form a group distinct froln metonymy which has characteristk:s that requires a quite different treatment; (b) chains of metonymies occur, supporting an observation by Reddy (1979); and (c) metonymies can co-occur with instances of either literalness, metaphor, or anomaly. An example is given of the computer analysis of a metonymy wh!h illustrates the above three featun:s. The example is analysed by a natural language program callext meta5 that uses Coilalive

A set of rules, named CSDC (Conjunct Scope Determination Constraints), is suggested for attacking the conjunct scope problem, the major issue in the automatic processing of conjunctions which has been raising great difficulty for natural language processing systems. Grammars embodying the CSDC are incorporated into an existing ATN parser, and are tested successfully against a wide group of "and" conjunctive sentences, which are of three types, namely clausal coordination, phrasal coordination, and gapping. With phrasal coordination the structure with two NPs coordinated by "and" has been given most attention.

This paper describes the application of so-called topic models to selectional preference induction. Three models related to Latent Dirichlet Allocation, a proven method for modelling document-word cooccurrences, are presented and evaluated on datasets of human plausibility judgements. Compared to previously proposed techniques, these models perform very competitively, especially for infrequent predicate-argument combinations where they exceed the quality of Web-scale predictions while using relatively little data. 1

In order to resolve metonymy and other violations of selectional restrictions between lexical items, a language understander must be able to infer relationships that do not have explicit lexical analogs in tile sentence. Although such inferencing has typically been relegated to the world knowledge portion of a natural language processing system, there is also evidence, from both theoretical analysis in compositional semantics and distributional analysis of corpus data, that some cases of metonymy may best be processed with respect to more specific lexical and syntactic constructions. In this paper, we argue how the richer vocabulary for lexical semantics proposed in Pustejovsky's "Generative Lexicon " theory allows one to explore the role of lexical information in such cases, and therefore sheds more light on the distinction between lexical inferences, which follow from defaults associated with lexical items and rules of composition, and pragmatic inferences, which depend on reasoning with respect to the context of the utterance. 1

This paper addresses the question of whether it is possible to sense-tag systematically, and on a large scale, and how we should assess progress so far. That is to say, how to attach each occurrence of a word in a text to one and only one sense in a dictionary---a particular dictionary of course, and that is part of the problem. The paper does not propose a solution to the question, though we have reported empirical findings elsewhere (Cowie et al. 1992 and Wilks et al. 1996), and intend to continue and refine that work. The point of this paper is to examine two well-known contributions critically, one (Kilgarriff 1993) which is widely taken as showing that the task, as defined, cannot be carried out systematically by humans, and secondly (Yarowsky 1995) which claims strikingly good results at doing exactly that. Introduction Empirical, corpus-based, computational linguistics reached by now into almost every crevice of the subject, and perhaps pragmatics will soon succumb. Semantics...

ViewGen, an algorithm and program for belief ascription, represents the beliefs of agents as explicit, partitioned proposition-sets known as environments. A way of extending View-Gen to the interpretation of metaphor, and in particular to the comprehension of metaphor within the belief spaces of particular agents, has been described elsewhere. The paper reports the further refinement and recent implementation of this approach, as well as summarizing the argument for the claim that ordinary non-metaphorical belief ascription and the transfer of information in metaphors can both be seen as different manifestations of a single environment-amalgamation process, one in which explicitly metaphorical amalgamations are triggered by "preference breaking " in the sentence being processed. This requires a consideration of the scoping of metaphor with respect to belief contexts, analogous to the scoping of quantification and definite descriptions with respect to such contexts. As a topic of ongoing and future work, the issue of mixed metaphor, of two distinct types, is briefly addressed. 1 ViewGen: The Basic Belief Engine A computational model of belief ascription is described in detail elsewhere [Wilks and Bien, 1979, 1983] [Ballim, 1987] [Wilks and Ballim, 1987] [Ballim and Wilks, in press] and is embodied in a prolog program called View-Gen. The basic algorithm of this model uses the notion of default reasoning to ascribe beliefs to other agents unless there is evidence to prevent the ascription. Perrault [1987, 1990] and Cohen and Levesque [1985] have also recently explored a belief and speech act logic based on a single explicit default axiom. As our previous work has shown for some years, the default ascription is basically correct, but the phenomena are more complex than are normally captured by an axiomatic approach. ViewGen also avoids certain counter-intuitive assumptions, such as the non-persistence of ignorance about any given proposition p [Perrault, 1990]. Also such systems