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

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

The Computer Revolution in Philosophy: Philosophy, Science and Models of Mind

The book presented deep important connections between Artificial Intelligence and Philosophy, based partly on an argument that both philosophy and science are primarily concerned with identifying and explaining possibilities contrary to a common view that science is primarily concerned with laws. The book attempted to show in principle how the construction, testing and debugging of complex computational models, explaining possibilities in a new way, can illuminate a collection of deep philosophical problems, e.g. about the nature of mind, the nature of representation, the nature of mathematical discovery. However it did not claim that this could be done easily or that the problems would be solved soon. 40 years later many of them have still not been solved, including explaining how biological brains made possible the deep mathematical discoveries made millennia ago, long before the development of modern logic, often wrongly assumed to provide foundations for all of mathematics. Later work on these ideas includes the author's Meta-Morphogenesis project, inspired by Alan Turing's work: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html Originally published in 1978 by Harvester Press, this went out of print. An electronic version was created from a scanned in copy and then extensively edited with corrections, addtional text, and notes, available online as html or pdf, intermittently updated. This pdf version was created on 2019/07/09

Automated Translation with Interlingual Word Representations

In dit proefschrift onderzoeken we het gebruik vertaalsystemen die gebruiken maken van een transferfase met interlinguale representaties van woorden. Op deze manier benaderen we het probleem van de lexicale ambiguïteit in de automatische vertaalsystemen als twee afzonderlijke taken: het bepalen van woordbetekenis en lexicale selectie. Eerst worden de woorden in de brontaal op basis van hun betekenis gedesambigueerd, resulterend in interlinguale representaties van woorden. Vervolgens wordt een lexicale selectiemodule gebruikt die het meest geschikte woord in de doeltaal selecteert. We geven een gedetailleerde beschrijving van de ontwikkeling en evaluatie van vertaalsystemen voor Nederlands-Engels. Dit biedt een achtergrond voor de experimenten in het tweede en derde deel van dit proefschrift. Daarna beschrijven we een methode die de betekenis van woorden bepaalt. Deze is vergelijkbaar met het klassieke Lesk-algoritme, omdat het gebruik maakt van het idee dat gedeelde woorden tussen de context van een woord en zijn definitie informatie over de betekenis ervan verschaffen. Wij gebruiken echter, in plaats daarvan, woord- en betekenisvectoren om de overeenkomst te berekenen tussen de definitie van een betekenis en de context van een woord. We gebruiken onze methode bovendien voor het localiseren en -interpreteren van woordgrapjes.Ten slotte presenteren we een model voor lexicale keuze dat lemma's selecteert, gegeven de abstracte representaties van woorden. Dit doen we door de grammaticale bomen te converteren naar hidden Markov bomen. Op deze manier kan de optimale combinatie van lemmas en hun context berekend worden

Unsupervised Induction of Frame-Based Linguistic Forms

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

The role of experience in common sense and expert problem solving

Issued as Progress reports [nos. 1-5], Reports [nos. 1-6], and Final report, Project no. G-36-617 (includes Projects nos. GIT-ICS-87/26, GIT-ICS-85/19, and GIT-ICS-85/18

Report Linking: Information Extraction for Building Topical Knowledge Bases

Human language artifacts represent a plentiful source of rich, unstructured information created by reporters, scientists, and analysts. In this thesis we provide approaches for adding structure: extracting and linking entities, events, and relationships from a collection of documents about a common topic. We pursue this linking at two levels of abstraction. At the document level we propose models for aligning the entities and events described in coherent and related discourses: these models are useful for deduplicating repeated claims, finding implicit arguments to events, and measuring semantic overlap between documents. Then at a higher level of abstraction, we construct knowledge graphs containing salient entities and relations linked to supporting documents: these graphs can be augmented with facts and summaries to give users a structured understanding of the information in a large collection

Natural language generation in the LOLITA system an engineering approach

Natural Language Generation (NLG) is the automatic generation of Natural Language (NL) by computer in order to meet communicative goals. One aim of NL processing (NLP) is to allow more natural communication with a computer and, since communication is a two-way process, a NL system should be able to produce as well as interpret NL text. This research concerns the design and implementation of a NLG module for the LOLITA system. LOLITA (Large scale, Object-based, Linguistic Interactor, Translator and Analyser) is a general purpose base NLP system which performs core NLP tasks and upon which prototype NL applications have been built. As part of this encompassing project, this research shares some of its properties and methodological assumptions: the LOLITA generator has been built following Natural Language Engineering principles uses LOLITA's SemNet representation as input and is implemented in the functional programming language Haskell. As in other generation systems the adopted solution utilises a two component architecture. However, in order to avoid problems which occur at the interface between traditional planning and realisation modules (known as the generation gap) the distribution of tasks between the planner and plan-realiser is different: the plan-realiser, in the absence of detailed planning instructions, must perform some tasks (such as the selection and ordering of content) which are more traditionally performed by a planner. This work largely concerns the development of the plan- realiser and its interface with the planner. Another aspect of the solution is the use of Abstract Transformations which act on the SemNet input before realisation leading to an increased ability for creating paraphrases. The research has lead to a practical working solution which has greatly increased the power of the LOLITA system. The research also investigates how NLG systems can be evaluated and the advantages and disadvantages of using a functional language for the generation task

Head-Driven Phrase Structure Grammar

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)