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<Paper uid="H89-2030">
  <Title>Belief Ascription and Model Generative Reasoning: joining two paradigms to a robust parser of messages.</Title>
  <Section position="3" start_page="0" end_page="221" type="intro">
    <SectionTitle>
INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> The purpose of this paper is to bring together the reasoning techniques used in the MGR and ViewGen systems and combine those with the PREMO robust parsing system (see references in separate sections below) to yield a combined approach to understanding text, particularly the interpretation or gisting of text which may be ill-formed, fragmented, stylized or even self-contradictory (when produced under stressful conditions, but evaluable against an appropriate knowledge base nonetheless). We also aim to build in pointers to the evaluation of the system as we go. Another guiding principle is to combine if possible, the benefits of being knowledge and situation based, when needed for interpretation, with the benefits that come from large scale automated analysis techniques drawn from the use of machine readable dictionaries and large text samples.</Paragraph>
    <Paragraph position="1"> We shall direct our examples where possible to realistic military scenarios drawn from other Army-sponsored work at CRL and constnact scenarios based on ship or troop unit sightings and casualty reports. However, our techniques are better, we hope, than the individual scenarios we can construct. In other cases we shall use the standard cases with which the systems have been developed.</Paragraph>
    <Paragraph position="2">  The overall aim is to produce a system that takes in noisy messages and data, has access to both Blue data-bases and military doctrine (in terms of beliefs and goals) and, in certain circumstances, access also to Red Army beliefs and goals. A number of possible scenarios are tentatively put forward at the end of the paper, in which the combination of the techniques discussed here could provide advice to, and a check upon the decisons of, the G2 officer in a Blue Army Corps, particularly in terms of the consistency of the advice offered the commander and consilience of the belief and data structures maintained at other lower levels of the system (e.g. Divisions).</Paragraph>
    <Paragraph position="3"> In the first three sections of the paper we summarize the PREMO, MGR and ViewGen techniques and then proceed to a discussion of how to link them: in general we envisage an overall system in which information moves (outward in the diagram below) from the robust parser PREMO, to the MGR, the generator of alternative scenarios/models, to ViewGen the point-of-view shell that considers the possible points of view in terms of the environments of other agents (e.g. Blue Generals view of Red General's view of a particular tank division). Both MGR and ViewGen also act as filters of possible but inapplicable models of the situation.</Paragraph>
    <Paragraph position="4"> PREMO: A ROBUST PARSER OF MESSAGES PREMO: the PREference Machine Organization is a knowledge-based Preference Semantics parser (Wilks 1972, 1975, 1978; Boguraev 1979; Carter 1984, 1987; Fass 1986, 1987, 1988; Huang 1984, 1988; Slator 1988a, 1988c) due to Brian Slator, with access to the large, text-specific, lexical semantic knowledge base created by the lexicon-provider of the CRL project on large scale lexical extraction from machine readable dictionaries (Wilks et al. in press). A fuller description of the relationship of PREMO to that project appears in (Slator &amp; Wilks 1989). PREMO is the parser we intend to use initially for initial processing of military reports (sightings, casualties etc.) that have been generated raapidiy under adverse  conditions.</Paragraph>
    <Paragraph position="5"> Preference Semantics is a theory of language in which the meaning for a text is represented by a complex semantic structure that is built up out of smaller semantic components; this composifionality is a fairly typical feature of semantic theories. The principal difference between Preference Semantics and other semantic theories is in the explicit and computational accounting of ambiguous, metaphorical, and linguistically non-standard language use; which is to say, it is intended to deal with the actual world of English texts outside linguistic studies, ranging from newspaper texts (well-formed but full of the non-standard phenomena just listed) to rapidly written diagostic reports, like machine-repair, or reports of sightings, which have all those features and are grammatically ill-formed in addition..</Paragraph>
    <Paragraph position="6"> The links between the components of the semantic structures are created on the basis of semantic preference and coherence. In text and discourse theory, coherence is generally taken to refer to the meaningfulness of text. Fass (1987) suggests that in NLP work such as Preference Semantics the notions of &amp;quot;satisfaction&amp;quot; and &amp;quot;violation&amp;quot; (of selection restrictions or preferences) and the notion of &amp;quot;semantic distance&amp;quot; (across structured type hierarchies) are different ways of characterising the meaningfulness of text; they capture different coherence relations. The original systems of Preference Semantics (Wilks 1972, 1975, 1978), were principally based on the coherence relation of &amp;quot;inclusion&amp;quot; (semantic preferences and selection restrictions); the emphasis in PREMO is more on the coherence relation based on semantic distance, although the original notions of coherence also survive.</Paragraph>
    <Paragraph position="7"> In Preference Semantics the semantic representation computed for a text is the one having the most semantically dense structure among the competing &amp;quot;readings.&amp;quot; Semantic density is a property of structures that have preferences regarding their own constituents, and satisfied preferences create density. Density is compared in terms of the existence of preference-matching features, the lack of preference-breaking features, and the length of the inference chains needed to justify each sense selection and constituent attachment decision. The job of a Preference Semantics parser, then, is to consider the various competing interpretations, of which there may be many, and to choose among them by finding the one that is the most semantically dense, and hence preferred.</Paragraph>
    <Paragraph position="8"> PREMO is a robust system for parsing natural language organized along the lines of an operating system. The state of every partial parse is captured in a &amp;quot;process control block&amp;quot; structure called a language object, and the control structure of the preference machine is a priority queue of these language objects. The language object at the front of the queue has the highest score as computed by a preference metric that weighs grammatical predictions, semantic type matching, and pragmatic coherence. The highest priority language object is the intermediate reading that is currently most preferred (the others are still &amp;quot;alive,&amp;quot; but not actively pursued); in this way the preference machine avoids combinatorial explosion by following a &amp;quot;best-first&amp;quot; strategy for parsing. Each &amp;quot;ready&amp;quot; process in the system captures the state of a partial parse with priority given to each parse &amp;quot;process&amp;quot; on the basis of a preference semantics evaluation. The &amp;quot;time-slice&amp;quot; for each process is whatever is needed to move forward one word in a local process sentence buffer (where each process operates on a private copy of the current sentence). After every time slice, the preference/priority for the currently &amp;quot;running&amp;quot; parse is re-computed and the language object for that process is returned to the priority queue. The first process to emerge from the queue with its sentence buffer empty is declared the winner and saved. This strategy is both a run-time optimization and an application of the &amp;quot;Least Effort Principle&amp;quot; of intuitively plausible language processing. The parsing is robust in that some structure is returned for every input, no matter how ill-formed or &amp;quot;garden-pathological&amp;quot; it is.</Paragraph>
    <Paragraph position="9"> A principal advance in PREMO over earlier Preference Semantics work is that it overcomes the semantic localism of that work that allowed peculiar but telling counter-examples to be created. A better-known one is due to Phil Hayes (quoted in Boden 1977): &amp;quot;He licked the gun all over and the stock tasted good&amp;quot;. A Preference Semantics system might well get &amp;quot;stock&amp;quot; resolved to &amp;quot;soup&amp;quot; rather than &amp;quot;gun stock&amp;quot; based on the local preferences of the last clause, because it had no overall GUN context, or indeed any way of expressing that. The frame movement of the Seventies was intended to provide such contextual theories (e.g. Minsky 1975) but it has not been generally accepted that it did so. The script-based parsers from Yale (e.g. Gershman, 1977) were created in the same topic-based spirit tended to fall into the opposite fault: of not understanding what was said unless it did wholly conform to expectations.</Paragraph>
    <Paragraph position="10">  PREMO is intendeeed to, and does, meet both requirements: the bottom-up coherence of preference combined by weightings with the topic-subject codes provided for LDOCE (Longmans Dictionary of Contemporary English, Procter et a1.1978) within the lexical acquisition project at CRL mentioned earlier.</Paragraph>
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