Fabio Crestani

Application of Spreading Activation Techniques in InformationRetrieval

This paper surveys the use of Spreading Activation techniques onSemantic Networks in Associative Information Retrieval. The majorSpreading Activation models are presented and their applications toIR is surveyed. A number of works in this area are criticallyanalyzed in order to study the relevance of Spreading Activation forassociative IR.

User's perception of relevance of spoken documents

We present the results of a study of users perception of relevance of documents. The aim is to study experimentally how users perception varies depending on the form that retrieved documents are presented. Documents retrieved in response to a query are presented to users in a variety of ways, from full text to a machine spoken query-biased automatically-generated summary, and the difference in users perception of relevance is studied. The experimental results suggest that the effectiveness of advanced multimedia Information Retrieval applications may be affected by the low level of users perception of relevance of retrieved documents.

On the use of information retrieval techniques for the automatic construction of hypertext

Abstract The first part of the paper briefly introduces what automatic authoring of a hypertext for information retrieval means. The most difficult part of the automatic construction of a hypertext is the creation of links connecting documents or document fragments that are simantically related. Because of this, to many researchers it seemed natural to use IR techniques for this purpose, since IR has always dealt with the construction of relationships between objects mutually relevant. The second part of the paper presents a survey of some of attempts toward the automatic construction of hypertexts for information retrieval. This part will identify and compare scope, advantages and limitations of different approaches. The aim of this survey is to point out the main and most successful current lines of research.

Design and implementation of a tool for the automatic construction of hypertexts for information retrieval

Abstract The paper describes the design and implementation of TACHIR, a tool for the automatic construction of hypertexts for Information Retrieval. Through the use of an authoring methodology employing a set of well known Information Retrieval techniques, TACHIR automatically builds up a hypertext from a document collection. The structure of the hypertext reflects a three level conceptual model that has proved to be quite effective for Information Retrieval. Using this model it is possible to navigate among documents, index terms, and concepts using automatically determined links. The hypertext is implemented using the HTML hypertext mark up language, the mark up language of the World Wide Web project. It can be distributed on different sites and different machines over the Internet, and it can be navigated using any of the interfaces developed in the framework World Wide Web project, for example NetScape .

Probability kinematics in information retrieval

In this paper we discuss the dynamics of probabilistic term weights in different IR retrieval models. We present four different models based on different notions of retrieval. Two of these models are classical probabilistic models long in use in IR, the two others are based on a logical technique of evaluating the probability of a conditional called Imaging, one is a generalisation of the other. We aualyse the transfer of probabilities occuring in the ~epresentation space at retrieval time for these four models, compare their retrieval performance using classical test collections, and discuss the results.

Vocal access to a newspaper archive: design issues and preliminary investigations

This paper presents the design and the current prototype implementation of an interactive vocal information retrieval system that can be used to access articles of a large newspaper archive using a telephone. The results of preliminary investigation into the feasibility of such a system are also presented.

A Model for Adaptive Information Retrieval

The paper presents a network model that can be used toproduce conceptual and logical schemas for Information Retrievalapplications. The model has interesting adaptability characteristicsand can be instantiated in various effective ways. The paper alsoreports the results of an experimental investigation into theeffectiveness of implementing associative and adaptive retrieval onthe proposed model by means of Neural Networks. The implementationmakes use of the learning and generalisation capabilities of theBackpropagation learning algorithm to build up and use applicationdomain knowledge in a sub-symbolic form. The knowledge is acquiredfrom examples of queries and relevant documents. Three differentlearning strategies are introduced, their performance is analysed andcompared with the performance of a traditional Information Retrievalsystem.

An Adaptive Information Retrieval System Based on Neural Networks

This paper presents partial results of an experimental investigation concerning the use of Neural Networks in associative adaptive Information Retrieval. The learning and generalisation capabilities of the Backpropagation learning procedure are used to build up and employ application domain knowledge in the form of a sub-symbolic knowledge representation. The knowledge is acquired from examples of queries and relevant documents of the collection. In this paper the architecture of the system is presented and the results of the experimentation are briefly reported.

Implementation and Evaluation of a Relevance Feedback Device Based on Neural Networks

This paper presents the results of an experimental investigation into the use of Neural Networks for implementing Relevance Feedback in an interactive Information Retrieval System. The most advance Relevance Feedback technique used in operative Interactive Information Retrieval systems, Probabilistic Relevance Feedback, is compared with a Neural Networks based technique. The latest uses the learning and generalisation capabilities of a 3-layer feedforward Neural Network with the Backpropagation learning procedure to learn distinguishing between relevant and non-relevant documents. A comparative evaluation between the two techniques is reported using an advance Information Retrieval System, a Neural Network simulator, and an IR test document collection. The results are reported and explained from an Information Retrieval point of view.