Spoken query processing for interactive information retrieval

It has long been recognised that interactivity improves the effectiveness of information retrieval systems. Speech is the most natural and interactive medium of communication and recent progress in speech recognition is making it possible to build systems that interact with the user via speech. However, given the typical length of queries submitted to information retrieval systems, it is easy to imagine that the effects of word recognition errors in spoken queries must be severely destructive on the system's effectiveness. The experimental work reported in this paper shows that the use of classical information retrieval techniques for spoken query processing is robust to considerably high levels of word recognition errors, in particular for long queries. Moreover, in the case of short queries, both standard relevance feedback and pseudo relevance feedback can be effectively employed to improve the effectiveness of spoken query processing

Vocal Access to a Newspaper Archive: Design Issues and Preliminary Investigation

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

Exploiting the similarity of non-matching terms at retrieval time

In classic information retrieval systems a relevant document will not be retrieved in response to a query if the document and query representations do not share at least one term. This problem, known as 'term mismatch', has been recognised for a long time by the information retrieval community and a number of possible solutions have been proposed. Here I present a preliminary investigation into a new class of retrieval models that attempt to solve the term mismatch problem by exploiting complete or partial knowledge of term similarity in the term space. The use of term similarity can enhance classic retrieval models by taking into account non-matching terms. The theoretical advantages and drawbacks of these models are presented and compared with other models tackling the same problem. A preliminary experimental investigation into the performance gain achieved by exploiting term similarity with the proposed models is presented and discussed

Multi-objective resource selection in distributed information retrieval

In a Distributed Information Retrieval system, a user submits a query to a broker, which determines how to yield a given number of documents from all possible resource servers. In this paper, we propose a multi-objective model for this resource selection task. In this model, four aspects are considered simultaneously in the choice of the resource: document's relevance to the given query, time, monetary cost, and similarity between resources. An optimized solution is achieved by comparing the performances of all possible candidates. Some variations of the basic model are also given, which improve the basic model's efficiency

Probabilistic learning for selective dissemination of information

New methods and new systems are needed to filter or to selectively distribute the increasing volume of electronic information being produced nowadays. An effective information filtering system is one that provides the exact information that fulfills user's interests with the minimum effort by the user to describe it. Such a system will have to be adaptive to the user changing interest. In this paper we describe and evaluate a learning model for information filtering which is an adaptation of the generalized probabilistic model of information retrieval. The model is based on the concept of 'uncertainty sampling', a technique that allows for relevance feedback both on relevant and nonrelevant documents. The proposed learning model is the core of a prototype information filtering system called ProFile

User centred evaluation of an automatically constructed hyper-textbook

As hypertext systems become widely available and their popularity increases, attention has turned to converting existing textual documents into hypertextual form. An important issue in this area is the fully automatic production of hypertext for learning, teaching, training, or self-referencing. Although many studies have addressed the problem of producing hyper-books, either manually or semi-automatically, the actual usability of hyper-books tools is still an area of ongoing research. This article presents an effort to investigate the effectiveness of a hyper-textbook for self-referencing produced in a fully automatic way. The hyper-textbook is produced using the Hyper-TextBook methodology. We developed a taskbased evaluation scheme and performed a comparative usercentred evaluation between a hyper-textbook and a conventional, printed form of the same textbook. The results indicate that the hyper-textbook, in most cases, improves speed, accuracy, and user satisfaction in comparison to the printed form of the textbook

Users' perception of relevance of spoken documents

We present the results of a study of user's 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