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Information retrieval using probabilistic techniques has at-tracted significant attention on the part of researchers in information and computer science over the past few de-cades. In the 198Os, knowledge-based techniques also made an impressive contribution to “intelligent ” informa-tion retrieval and indexing. More recently, information sci-ence researchers have turned to other newer artificial-in-telligence-based inductive learning techniques including neural networks, symbolic learning, and genetic algo-rithms. These newer techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and re-trieval capabilities of current information storage and re-trieval systems. In this article, we first provide an overview of these newer techniques and their use in information science research. To familiarize readers with these tech-niques, we present three popular methods: the connec-tionist Hopfield network; the symbolic ID3/ID5R; and evolu-tion-based genetic algorithms. We discuss their knowl-edge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique. We believe these techniques are promising in their ability to analyze user queries, identify users ’ infor-mation needs, and suggest alternatives for search. With proper user-system interactions, these methods can greatly complement the prevailing full-text, keyword-based, probabilistic, and knowledge-based techniques.

This research presents an algorithmic approach to addressing the vocabulary problem in scientific information retrieval and information sharing, using the molecular biology domain as an example. We first present a literature review of cognitive stud!es related to the vcrcabulaw problem and vocabulary-based search aids (thesauri) and then discuss technques for building robust and domain-specific thesauri to assist in cross-domain scientific information retrieval. Using a variation of the automatic thesaurus generation techniques, which we refer to as the concept space approach, we racentiy conducted an experiment in the molecular biology domain in whch we created a C. eksgans worm thesaurus of 7,657 worm-specific terms and a Drosophila fty thesaurus of 15,626 terms. About 30 % of these terms overtappad, which created vocabulary paths

: This paper presents the INFOCLAS system applying statistical methods of information retrieval for the classification of German business letters into corresponding message types such as order, offer, enclosure, etc. INFOCLAS is a first step towards the understanding of documents proceeding to a classification-driven extraction of information. The system is composed of two main modules: the central indexer (extraction and weighting of indexing terms) and the classifier (classification of business letters into given types). The system employs several knowledge sources including a letter database, word frequency statistics for German, lists of message type specific words, morphological knowledge as well as the underlying document structure. As output, the system evaluates a set of weighted hypotheses about the type of the actual letter. Classification of documents allow the automatic distribution or archiving of letters and is also an excellent starting point for higher-level...

Digital libraries (DLs) are complex information systems and therefore demand formal foundations lest development e#orts diverge and interoperability su#ers. In this paper, we propose the fundamental abstractions of Streams, Structures, Spaces, Scenarios, and Societies (5S), which contribute to define digital libraries rigorously and usefully. Streams are sequences of abstract items used to describe static and dynamic content. Structures can be defined as labeled directed graphs, which impose organization. Spaces are sets of abstract items and operations on those sets that obey certain rules. Scenarios consist of sequences of events or actions that modify states of a computation in order to accomplish a functional requirement. Societies comprehend entities and the relationships between and among them. Together these abstractions relate and unify concepts, among others, of digital objects, metadata, collections, and services required to formalize and elucidate "digital libraries". The applicability, versatility and unifying power of the theory is demonstrated through its use in three distinct applications: building and interpretation of a DL taxonomy, analysis of case studies of digital libraries, and utilization as a formal basis for a DL description language. Keywords: digital libraries, theory, foundations, definitions, applications 1 1 Motivation Digital libraries are extremely complex information systems. The proper concept of a digital library seems hard to completely understand and evades definitional consensus. Di#erent views (e.g., historical, technological) and perspectives (e.g., from the library and information science, information retrieval, or human-computer interaction communities) have led to a myriad of di#ering definitions. Licklider, in his seminal ...

This report describes research to evaluate a theory of belief revision proposed by Galliers in the context of information-seeking interaction as modelled by Belkin, Brooks and Daniels and illustrated by user-librarian dialogues. The work covered the detailed assessment and development, and computational implementation and testing, of both the belief revision theory and the information retrieval model. Some features of the belief theory presented problems, and the original `multiple expert' retrieval model had to be drastically modified to support rational dialogue management. But the experimental results showed that the characteristics of literature-seeking interaction could be successfully captured by the belief theory, exploiting important elements of the retrieval model. Thus though the system's knowledge and dialogue performance were very limited, it provides a useful base for further research. The report presents all aspects of the research in detail, with particular emphasis on the implementation of belief and intention revision, and the integration of revision with domain reasoning and dialogue interaction.

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This paper studies the extraction and transformation problem on documents. Solving this problem entails extracting the structures contained in a document and transforming the structures to make them available for further automatic processing. This paper provides an overview of methods and tools for extracting information and transforming the extracted information as required by the end-user. The overview is based on a taxonomy that classifies both characteristics of the available sources and properties of the extraction techniques. The paper concludes with a perspective on future developments, including a discussion of tools with learning capabilities and the role that XML and other related standards will play in the future.

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Within the framework of the Information Retrieval System DIALECT 2, we propose a connectionist method for a linguistic morpho-syntactic parsing of French Language. The system is based upon a three layers Neural Network with a recursive sentence structure. This network is in charge of the acquisition of Natural Language grammatical competence, in order to do the parsing of the sentences. The learning stage is supervised and distributed into several levels. The learning algorithm uses a measure grounded on an Entropic computation. In this report, we describe the overall architecture of the system. Then we show the first results obtained with samples made up with sentences from schoolbooks for children who are taught reading. Keywords Entropy, Grammar, Learning, Linguistic Parsing, Natural Language, Neural Networks, Recurrent Networks. sur mes cahiers d'ecolier, sur mon pupitre et les arbres sur le sable sur la neige j'e cris ton nom sur toutes les pages blanches sur toutes les pages ...