10 research outputs found

    An investigation into the theoretical foundations of library cataloguing and a critical analysis of the cataloguing of the South African national bibliography, 1981-1983

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    Includes bibliographical references.This thesis proposes that the foundations of the library catalogue are not rooted in a coherent, encompassing and comprehensive theoretical structure. Instead, it shows that it rests upon a number of principles that evolved during the nineteenth century from the work done by cataloguing experts such as Panizzi, Jewett and Cutter. These principles are shown to be either principles of access or of bibliographical description, and they still form the basis for the construction of modern catalogues according to the Anglo-American Cataloguing Rules, 2nd edition (AACR2). The South African National Bibliography (SANB) is then used as an example of an actual catalogue constructed according to the AACR2. A study is conducted of the cataloguing records in the SANB in order to establish how these Rules are put into practice, and how usable a catalogue may be produced according to these Rules and principles. It is concluded that the SANB is a high quality catalogue according to the standards set by the AACR2, but that such a catalogue may not be optimally useful from the point of view of the user. Certain ideas from Artificial Intelligence are then employed to find out to what extent a user is able to utilize the library catalogue as a channel of communication in order to gain maximum benefit from the information available in the catalogue. It is found that the user is indeed not equipped to make full use of the catalogue, and it is suggested that the potential for increased access facilities brought (v) about by computer technology may be employed to bridge the communication gap between the user and the cataloguer. The thesis therefore concludes that the established principles according to which catalogues are constructed, are inadequate for the formulation of a comprehensive theory of cataloguing, but a search for such a theory is shown to be ultimately inappropriate. Cataloguing is essentially a problem-solving pursuit which aims at the production of a tangible object; a usable catalogue. Modern computer technology has brought the library catalogue to a crossroads in its development, and a detailed study of user needs will have to form the basis for the development of additional principles according to which the new technology will most successfully be applied to library catalogues

    Unsupervised Induction of Frame-Based Linguistic Forms

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    This thesis studies the use of bulk, structured, linguistic annotations in order to perform unsupervised induction of meaning for three kinds of linguistic forms: words, sentences, and documents. The primary linguistic annotation I consider throughout this thesis are frames, which encode core linguistic, background or societal knowledge necessary to understand abstract concepts and real-world situations. I begin with an overview of linguistically-based structured meaning representation; I then analyze available large-scale natural language processing (NLP) and linguistic resources and corpora for their abilities to accommodate bulk, automatically-obtained frame annotations. I then proceed to induce meanings of the different forms, progressing from the word level, to the sentence level, and finally to the document level. I first show how to use these bulk annotations in order to better encode linguistic- and cognitive science backed semantic expectations within word forms. I then demonstrate a straightforward approach for learning large lexicalized and refined syntactic fragments, which encode and memoize commonly used phrases and linguistic constructions. Next, I consider two unsupervised models for document and discourse understanding; one is a purely generative approach that naturally accommodates layer annotations and is the first to capture and unify a complete frame hierarchy. The other conditions on limited amounts of external annotations, imputing missing values when necessary, and can more readily scale to large corpora. These discourse models help improve document understanding and type-level understanding

    Head-Driven Phrase Structure Grammar

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    Head-Driven Phrase Structure Grammar (HPSG) is a constraint-based or declarative approach to linguistic knowledge, which analyses all descriptive levels (phonology, morphology, syntax, semantics, pragmatics) with feature value pairs, structure sharing, and relational constraints. In syntax it assumes that expressions have a single relatively simple constituent structure. This volume provides a state-of-the-art introduction to the framework. Various chapters discuss basic assumptions and formal foundations, describe the evolution of the framework, and go into the details of the main syntactic phenomena. Further chapters are devoted to non-syntactic levels of description. The book also considers related fields and research areas (gesture, sign languages, computational linguistics) and includes chapters comparing HPSG with other frameworks (Lexical Functional Grammar, Categorial Grammar, Construction Grammar, Dependency Grammar, and Minimalism)

    Head-Driven Phrase Structure Grammar

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    Head-Driven Phrase Structure Grammar (HPSG) is a constraint-based or declarative approach to linguistic knowledge, which analyses all descriptive levels (phonology, morphology, syntax, semantics, pragmatics) with feature value pairs, structure sharing, and relational constraints. In syntax it assumes that expressions have a single relatively simple constituent structure. This volume provides a state-of-the-art introduction to the framework. Various chapters discuss basic assumptions and formal foundations, describe the evolution of the framework, and go into the details of the main syntactic phenomena. Further chapters are devoted to non-syntactic levels of description. The book also considers related fields and research areas (gesture, sign languages, computational linguistics) and includes chapters comparing HPSG with other frameworks (Lexical Functional Grammar, Categorial Grammar, Construction Grammar, Dependency Grammar, and Minimalism)

    Intelligent decision support systems for optimised diabetes

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    Computers now pervade the field of medicine extensively; one recent innovation is the development of intelligent decision support systems for inexperienced or non-specialist pbysicians, or in some cases for use by patients. In this thesis a critical review of computer systems in medicine, with special reference to decision support systems, is followed by a detailed description of the development and evaluation of two new, interacting, intelligent decision support systems in the domain of diabetes. Since the discovery of insulin in 1922, insulin replacement therapy for the treatment of diabetes mellitus bas evolved into a complex process; there are many different formulations of insulin and much more information about the factors which affect patient management (e.g. diet, exercise and progression of complications) are recognised. Physicians have to decide on the most appropriate anti-diabetic therapy to prescribe to their patients. Insulin-treated patients also have to monitor their blood glucose and decide how much insulin to inject and when to inject it. In order to help patients determine the most appropriate dose of insulin to take, a simple-to-use, hand-held decision support system has been developed. Algorithms for insulin adjustment have been elicited and combined with general rules of therapy to offer advice for every dose. The utility of the system has been evaluated by clinical trials and simulation studies. In order to aid physician management, a clinic-based decision support system has also been developed. The system provides wide-ranging advice on all aspects of diabetes care and advises an appropriate therapy regimen according to individual patient circumstances. Decisions advised by the pbysician-related system have been evaluated by a panel of expert physicians and the system has undergone informal primary evaluation within the clinic setting. An interesting aspect of both systems is their ability to provide advice even in cases where information is lacking or uncertain

    Knowledge based approach to process engineering design

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    A treatise on language methods and language-games in autism

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    Although it is generally understood that autism is a developmental disability affecting social learning, my social constructionist perspective suggested to me that, strangely, current theories aimed at explaining the nature of autism appeared not to fully reflect the essential social aspects of autism. Given that typically developing human beings become fully socialised through learning a first language, it appeared to me that autism research has, especially of late, failed to give sufficient attention to language despite Kanner’s advice. In researching this thesis I have sought to make a contribution to knowledge of my subject by: (1) developing a synthesis of current knowledge of autistic language methods as a practical framework to guide future research focused on language in autism; (2) critiquing ‘established’ autism theory; (3) drawing attention to Ludwig Wittgenstein’s neglected contributions to the philosophy of mind; and (4) reviewing the contribution of ‘alternative’ theory, including Wittgenstein’s criteriological theory, to an understanding of autism. My research has involved reviewing: (a) the literature on autistic language methods; (b)Conversation Analysis of autistic conversation; (c) narrative writing by authors diagnosed or retrospectively diagnosed with autism; and (d) existing autism theory. I conclude that there are specific features of talk and writing that reflect autism with some features of autistic writing being a ‘mirror image’ of features of autistic talk. A further, important, conclusion is that there are strengths as well as weaknesses associated with autistic talk and writing i.e., from a linguistic stance, it is wrong to regard autism as a disability; rather, it involves a different way of communicating – both verbally and in writing – than is seen in typically developing people. I also conclude that alternative theory has much to contribute to an understanding of autism, and that the atypical nature of autistic social development results in autistic people failing to fully come to terms with language-games

    A source modelling system and its use for uncertainty management

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    Human agents have to deal with a considerable amount of information from their environment and are also continuously faced with the need to take actions. As that information is largely of an uncertain nature, human agents have to decide whether, or how much, to believe individual pieces of information. To enable a reasoning system to deal in general with the demands of a real environment, and with information from human sources in particular, requires tools for uncertainty management and belief formation. This thesis presents a model for the management of uncertain information from human sources. Dealing, more specifically, with information which has been pre-processed by a natural language processor and transformed into an event-based representation, the model assesses information, forms beliefs and resolves conflicts between them in order to maintain a consistent world model. The approach is built on the fundamental principle that the uncertainty of information from people can, in the majority of situations, successfully be assessed through source models which record factors concerning the source's abilities and trustworthiness. These models are adjusted to reflect changes in the behaviour of the source. A mechanism is presented together with the underlying principles to reproduce such a behaviour. A high-level design is also given to make the proposed model reconstructible, and the successful operation of the model is demonstrated on two detailed examples

    A predicated network formalism for commonsense reasoning.

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    Chiu, Yiu Man Edmund.Thesis submitted in: December 1999.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 269-248).Abstracts in English and Chinese.Abstract --- p.iAcknowledgments --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- The Beginning Story --- p.2Chapter 1.2 --- Background --- p.3Chapter 1.2.1 --- History of Nonmonotonic Reasoning --- p.3Chapter 1.2.2 --- Formalizations of Nonmonotonic Reasoning --- p.6Chapter 1.2.3 --- Belief Revision --- p.13Chapter 1.2.4 --- Network Representation of Knowledge --- p.17Chapter 1.2.5 --- Reference from Logic Programming --- p.21Chapter 1.2.6 --- Recent Work on Network-type Automatic Reasoning Sys- tems --- p.22Chapter 1.3 --- A Novel Inference Network Approach --- p.23Chapter 1.4 --- Objectives --- p.23Chapter 1.5 --- Organization of the Thesis --- p.24Chapter 2 --- The Predicate Inference Network PIN --- p.25Chapter 2.1 --- Preliminary Terms --- p.26Chapter 2.2 --- Overall Structure --- p.27Chapter 2.3 --- Object Layer --- p.29Chapter 2.3.1 --- Virtual Object --- p.31Chapter 2.4 --- Predicate Layer --- p.33Chapter 2.4.1 --- Node Values --- p.34Chapter 2.4.2 --- Information Source --- p.35Chapter 2.4.3 --- Belief State --- p.36Chapter 2.4.4 --- Predicates --- p.37Chapter 2.4.5 --- Prototypical Predicates --- p.37Chapter 2.4.6 --- Multiple Inputs for a Single Belief --- p.39Chapter 2.4.7 --- External Program Call --- p.39Chapter 2.5 --- Variable Layer --- p.40Chapter 2.6 --- Inter-Layer Links --- p.42Chapter 2.7 --- Chapter Summary --- p.43Chapter 3 --- Computation for PIN --- p.44Chapter 3.1 --- Computation Functions for Propagation --- p.45Chapter 3.1.1 --- Computational Functions for Combinative Links --- p.45Chapter 3.1.2 --- Computational Functions for Alternative Links --- p.49Chapter 3.2 --- Applying the Computation Functions --- p.52Chapter 3.3 --- Relations Represented in PIN --- p.55Chapter 3.3.1 --- Relations Represented by Combinative Links --- p.56Chapter 3.3.2 --- Relations Represented by Alternative Links --- p.59Chapter 3.4 --- Chapter Summary --- p.61Chapter 4 --- Dynamic Knowledge Update --- p.62Chapter 4.1 --- Operations for Knowledge Update --- p.63Chapter 4.2 --- Logical Expression --- p.63Chapter 4.3 --- Applicability of Operators --- p.64Chapter 4.4 --- Add Operation --- p.65Chapter 4.4.1 --- Add a fully instantiated single predicate proposition with no virtual object --- p.66Chapter 4.4.2 --- Add a fully instantiated pure disjunction --- p.68Chapter 4.4.3 --- Add a fully instantiated expression which is a conjunction --- p.71Chapter 4.4.4 --- Add a human biased relation --- p.74Chapter 4.4.5 --- Add a single predicate expression with virtual objects --- p.76Chapter 4.4.6 --- Add a IF-THEN rule --- p.80Chapter 4.5 --- Remove Operation --- p.88Chapter 4.5.1 --- Remove a Belief --- p.88Chapter 4.5.2 --- Remove a Rule --- p.91Chapter 4.6 --- Revise Operation --- p.94Chapter 4.6.1 --- Revise a Belief --- p.94Chapter 4.6.2 --- Revise a Rule --- p.96Chapter 4.7 --- Consistency Maintenance --- p.97Chapter 4.7.1 --- Logical Suppression --- p.98Chapter 4.7.2 --- Example on Handling Inconsistent Information --- p.99Chapter 4.8 --- Chapter Summary --- p.102Chapter 5 --- Knowledge Query --- p.103Chapter 5.1 --- Domains of Quantification --- p.104Chapter 5.2 --- Reasoning through Recursive Rules --- p.109Chapter 5.2.1 --- Infinite Looping Control --- p.110Chapter 5.2.2 --- Proof of the finite termination of recursive rules --- p.111Chapter 5.3 --- Query Functions --- p.117Chapter 5.4 --- Type I Queries --- p.119Chapter 5.4.1 --- Querying a Simple Single Predicate Proposition (Type I) --- p.122Chapter 5.4.2 --- Querying a Belief with Logical Connective(s) (Type I) --- p.128Chapter 5.5 --- Type II Queries --- p.132Chapter 5.5.1 --- Querying Single Predicate Expressions (Type II) --- p.134Chapter 5.5.2 --- Querying an Expression with Logical Connectives (Type II) --- p.143Chapter 5.6 --- Querying an Expression with Virtual Objects --- p.152Chapter 5.6.1 --- Type I Queries Involving Virtual Object --- p.152Chapter 5.6.2 --- Type II Queries involving Virtual Objects --- p.156Chapter 5.7 --- Chapter Summary --- p.157Chapter 6 --- Uniqueness and Finite Termination --- p.159Chapter 6.1 --- Proof Structure --- p.160Chapter 6.2 --- Proof for Completeness and Finite Termination of Domain Search- ing Procedure --- p.161Chapter 6.3 --- Proofs for Type I Queries --- p.167Chapter 6.3.1 --- Proof for Single Predicate Expressions --- p.167Chapter 6.3.2 --- Proof of Type I Queries on Expressions with Logical Con- nectives --- p.172Chapter 6.3.3 --- General Proof for Type I Queries --- p.174Chapter 6.4 --- Proofs for Type II Queries --- p.175Chapter 6.4.1 --- Proof for Type II Queries on Single Predicate Expressions --- p.176Chapter 6.4.2 --- Proof for Type II Queries on Disjunctions --- p.178Chapter 6.4.3 --- Proof for Type II Queries on Conjunctions --- p.179Chapter 6.4.4 --- General Proof for Type II Queries --- p.181Chapter 6.5 --- Proof for Queries Involving Virtual Objects --- p.182Chapter 6.6 --- Uniqueness and Finite Termination of PIN Queries --- p.183Chapter 6.7 --- Chapter Summary --- p.184Chapter 7 --- Lifschitz's Benchmark Problems --- p.185Chapter 7.1 --- Structure --- p.186Chapter 7.2 --- Default Reasoning --- p.186Chapter 7.2.1 --- Basic Default Reasoning --- p.186Chapter 7.2.2 --- Default Reasoning with Irrelevant Information --- p.187Chapter 7.2.3 --- Default Reasoning with Several Defaults --- p.188Chapter 7.2.4 --- Default Reasoning with a Disabled Default --- p.190Chapter 7.2.5 --- Default Reasoning in Open Domain --- p.191Chapter 7.2.6 --- Reasoning about Unknown Exceptions I --- p.193Chapter 7.2.7 --- Reasoning about Unknown Exceptions II --- p.194Chapter 7.2.8 --- Reasoning about Unknown Exceptions III --- p.196Chapter 7.2.9 --- Priorities between Defaults --- p.198Chapter 7.2.10 --- Priorities between Instances of a Default --- p.199Chapter 7.2.11 --- Reasoning about Priorities --- p.199Chapter 7.3 --- Inheritance --- p.200Chapter 7.3.1 --- Linear Inheritance --- p.200Chapter 7.3.2 --- Tree-Structured Inheritance --- p.202Chapter 7.3.3 --- One-Step Multiple Inheritance --- p.203Chapter 7.3.4 --- Multiple Inheritance --- p.204Chapter 7.4 --- Uniqueness of Names --- p.205Chapter 7.4.1 --- Unique Names Hypothesis for Objects --- p.205Chapter 7.4.2 --- Unique Names Hypothesis for Functions --- p.206Chapter 7.5 --- Reasoning about Action --- p.206Chapter 7.6 --- Autoepistemic Reasoning --- p.206Chapter 7.6.1 --- Basic Autoepistemic Reasoning --- p.206Chapter 7.6.2 --- Autoepistemic Reasoning with Incomplete Information --- p.207Chapter 7.6.3 --- Autoepistemic Reasoning with Open Domain --- p.207Chapter 7.6.4 --- Autoepistemic Default Reasoning --- p.208Chapter 8 --- Comparison with PROLOG --- p.214Chapter 8.1 --- Introduction of PROLOG --- p.215Chapter 8.1.1 --- Brief History --- p.215Chapter 8.1.2 --- Structure and Inference --- p.215Chapter 8.1.3 --- Why Compare PIN with Prolog --- p.216Chapter 8.2 --- Representation Power --- p.216Chapter 8.2.1 --- Close World Assumption and Negation as Failure --- p.216Chapter 8.2.2 --- Horn Clauses --- p.217Chapter 8.2.3 --- Quantification --- p.218Chapter 8.2.4 --- Build-in Functions --- p.219Chapter 8.2.5 --- Other Representation Issues --- p.220Chapter 8.3 --- Inference and Query Processing --- p.220Chapter 8.3.1 --- Unification --- p.221Chapter 8.3.2 --- Resolution --- p.222Chapter 8.3.3 --- Computation Efficiency --- p.225Chapter 8.4 --- Knowledge Updating and Consistency Issues --- p.227Chapter 8.4.1 --- PIN and AGM Logic --- p.228Chapter 8.4.2 --- Knowledge Merging --- p.229Chapter 8.5 --- Chapter Summary --- p.229Chapter 9 --- Conclusion and Discussion --- p.230Chapter 9.1 --- Conclusion --- p.231Chapter 9.1.1 --- General Structure --- p.231Chapter 9.1.2 --- Representation Power --- p.231Chapter 9.1.3 --- Inference --- p.232Chapter 9.1.4 --- Dynamic Update and Consistency --- p.233Chapter 9.1.5 --- Soundness and Completeness Versus Efficiency --- p.233Chapter 9.2 --- Discussion --- p.234Chapter 9.2.1 --- Different Selection Criteria --- p.234Chapter 9.2.2 --- Link Order --- p.235Chapter 9.2.3 --- Inheritance Reasoning --- p.236Chapter 9.3 --- Future Work --- p.237Chapter 9.3.1 --- Implementation --- p.237Chapter 9.3.2 --- Application --- p.237Chapter 9.3.3 --- Probabilistic and Fuzzy PIN --- p.238Chapter 9.3.4 --- Temporal Reasoning --- p.238Bibliography --- p.23
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