A model for structured document retrieval : empirical investigations

Documents often display a structure, e.g., several sections, each with several subsections and so on. Taking into account the structure of a document allows the retrieval process to focus on those parts of the document that are most relevant to an information need. In previous work, we developed a model for the representation and the retrieval of structured documents. This paper reports the first experimental study of the effectiveness and applicability of the model

Report on the Glasgow IR group (glair4) submission

This year's submission from the Glasgow IR group (glair4) is to the category B automatic ad hoc section. Due to pressures of time and unexpected complications, our intended application of a technique known as generalised imaging [Crestani 95] was not completed in time for the TREC deadline. Therefore, the submission is the output of an IR system running a simplistic retrieval strategy, similar to last year's submission though with some intended improvements. It would appear from comparison with other category B submissions that this strategy is relatively successful. The following sections of this report contain a description of the retrieval strategy used, a analysis of the results, and finally, a discussion of our intentions for TREC 6

A framework for investigating the interaction in information retrieval

To increase retrieval effectiveness, information retrieval systems must offer better supports to users in their information seeking activities. To achieve this, one major concern is to obtain a better understanding of the nature of the interaction between a user and an information retrieval system. For this, we need a means to analyse the interaction in information retrieval, so as to compare the interaction processes within and across information retrieval systems. We present a framework for investigating the interaction between users and information retrieval systems. The framework is based on channel theory, a theory of information and its flow, which provides an explicit ontology that can be used to represent any aspect of the interaction process. The developed framework allows for the investigation of the interaction in information retrieval at the desired level of abstraction. We use the framework to investigate the interaction in relevance feedback and standard web search

Ranking social bookmarks using topic models

Ranking of resources in social tagging systems is a difficult problem due to the inherent sparsity of the data and the vo- cabulary problems introduced by having a completely unre- stricted lexicon. In this paper we propose to use hidden topic models as a principled way of reducing the dimensionality of this data to provide more accurate resource rankings with higher recall. We first describe Latent Dirichlet Allocation (LDA) and then show how it can be used to rank resources in a social bookmarking system. We test the LDA tagging model and compare it with 3 non-topic model baselines on a large data sample obtained from the Delicious social book- marking site. Our evaluations show that our LDA-based method significantly outperforms all of the baselines

A retrieval evaluation methodology for incomplete relevance assessments

In this paper we a propose an extended methodology for laboratory based Information Retrieval evaluation under in complete relevance assessments. This new protocol aims to identify potential uncertainty during system comparison that may result from incompleteness. We demonstrate how this methodology can lead towards a finer grained analysis of systems. This is advantageous, because the detection of uncertainty during the evaluation process can guide and direct researchers when evaluating new systems over existing and future test collections

Retrieval through explanation : an abductive inference approach to relevance feedback

Relevance feedback techniques are designed to automatically improve a system's representation of a query by using documents the user has marked as relevant. However, traditional relevance feedback models suffer from a number of limitations that restrict their potential in supporting information seeking. One of the major limitations of relevance feedback is that it does not incorporate behavioural aspects of information seeking - how and why users assess relevance. We propose that relevance feedback should be viewed as a process of explanation and demonstrate how this limitation of relevance feedback techniques can be overcome by a theory of relevance feedback based on abductive inference

Combining and selecting characteristics of information use

In this paper we report on a series of experiments designed to investigate the combination of term and document weighting functions in Information Retrieval. We describe a series of weighting functions, each of which is based on how information is used within documents and collections, and use these weighting functions in two types of experiments: one based on combination of evidence for ad-hoc retrieval, the other based on selective combination of evidence within a relevance feedback situation. We discuss the difficulties involved in predicting good combinations of evidence for ad-hoc retrieval, and suggest the factors that may lead to the success or failure of combination. We also demonstrate how, in a relevance feedback situation, the relevance assessments can provide a good indication of how evidence should be selected for query term weighting. The use of relevance information to guide the combination process is shown to reduce the variability inherent in combination of evidence

Assessing fun: young children as evaluators of interactive systems.

In this paper, we describe an exploratory study on the challenges of conducting usability tests with very young children aged 3 to 4 years old (nursery age) and the differences when working with older children aged 5 to 6 years old (primary school). A pilot study was conducted at local nursery and primary schools to understand and experience the challenges working with young children interacting with computer products. We report on the studies and compare the experiences of working with children of different age groups in evaluation studies of interactive systems

A study of factors affecting the utility of implicit relevance feedback

Implicit relevance feedback (IRF) is the process by which a search system unobtrusively gathers evidence on searcher interests from their interaction with the system. IRF is a new method of gathering information on user interest and, if IRF is to be used in operational IR systems, it is important to establish when it performs well and when it performs poorly. In this paper we investigate how the use and effectiveness of IRF is affected by three factors: search task complexity, the search experience of the user and the stage in the search. Our findings suggest that all three of these factors contribute to the utility of IRF

The technological impact of diffusion in nanopores

The impact of nanopore diffusion on the performance of adsorption separation processes is reviewed. Zeolite membrane processes and kinetically selective cyclic adsorption processes depend for their selectivity on differences in intracrystalline diffusion rates so these processes are designed to operate under conditions of intracrystalline diffusion control. In contrast, the performance of equilibrium based adsorption separation processes is adversely affected by diffusional resistance so in such processes the minimization of all resistances to mass transfer is a major design objective. Zeolite catalyzed reactions constitute a further important class of processes in which intrusion of diffusional resistance can be either advantageous or disadvantageous. Such effects are illustrated by considering in detail the conversion of methanol to light olefins (MTO) over SAPO34. Within the chemical process industries diffusion is important over a wide range of length scales. In this paper we focus only on diffusion at the nanometer scale since diffusional phenomena on this scale are critically important in adsorption separation processes as well as in many heterogeneous catalytic systems. Indeed membrane separations and molecular sieving adsorption processes (kinetic separations) are driven by differences in nanoscale diffusivities. For such processes the conditions of operation must therefore be selected so as to maximize the influence of nanoscale diffusion. This is true also for certain catalytic processes in which product selectivity can sometimes be improved by operating under conditions of diffusion control. More commonly, in equilibrium controlled adsorptive separations and in catalytic systems where activity rather than selectivity is the important feature, process performance is adversely affected by nanoscale diffusion, and in such systems it is obviously desirable to design the process in such a way as to minimize the intrusion of diffusional resistances. Some examples of both classes of process are discussed below