Search procedures revisited

Search Procedures reflects on a series of studies carried out over a four year period in the late 1970s. It was published at an interesting time for Information Retrieval. Written before Information Retrieval became synonymous with online information seeking it focuses on Information Retrieval within Public Libraries, then the major location for everyday information seeking. While many of his contemporaries focused on information seeking in academic or special library settings, Peter chose instead to focus a setting that was visited by a more diverse set of people with a broader range of information needs

Meeting of the MINDS: an information retrieval research agenda

Since its inception in the late 1950s, the field of Information Retrieval (IR) has developed tools that help people find, organize, and analyze information. The key early influences on the field are well-known. Among them are H. P. Luhn's pioneering work, the development of the vector space retrieval model by Salton and his students, Cleverdon's development of the Cranfield experimental methodology, Spärck Jones' development of idf, and a series of probabilistic retrieval models by Robertson and Croft. Until the development of the WorldWideWeb (Web), IR was of greatest interest to professional information analysts such as librarians, intelligence analysts, the legal community, and the pharmaceutical industry

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

Combining Terrier with Apache Spark to Create Agile Experimental Information Retrieval Pipelines

Experimentation using IR systems has traditionally been a procedural and laborious process. Queries must be run on an index, with any parameters of the retrieval models suitably tuned. With the advent of learning-to-rank, such experimental processes (including the appropriate folding of queries to achieve cross-fold validation) have resulted in complicated experimental designs and hence scripting. At the same time, machine learning platforms such as Scikit Learn and Apache Spark have pioneered the notion of an experimental pipeline , which naturally allows a supervised classification experiment to be expressed a series of stages, which can be learned or transformed. In this demonstration, we detail Terrier-Spark, a recent adaptation to the Terrier Information Retrieval platform which permits it to be used within the experimental pipelines of Spark. We argue that this (1) provides an agile experimental platform for information retrieval, comparable to that enjoyed by other branches of data science; (2) aids research reproducibility in information retrieval by facilitating easily-distributable notebooks containing conducted experiments; and (3) facilitates the teaching of information retrieval experiments in educational environments

Evaluating epistemic uncertainty under incomplete assessments

The thesis of this study is to propose an extended methodology for laboratory based Information Retrieval evaluation under incomplete relevance assessments. This new methodology aims to identify potential uncertainty during system comparison that may result from incompleteness. The adoption of this methodology is advantageous, because the detection of epistemic uncertainty - the amount of knowledge (or ignorance) we have about the estimate of a system's performance - during the evaluation process can guide and direct researchers when evaluating new systems over existing and future test collections. Across a series of experiments we demonstrate how this methodology can lead towards a finer grained analysis of systems. In particular, we show through experimentation how the current practice in Information Retrieval evaluation of using a measurement depth larger than the pooling depth increases uncertainty during system comparison

Report on the first Twente Data Management Workshop on XML Databases and Information Retrieval

The Database Group of the University of Twente initiated a new series of workshops called Twente Data Management workshops (TDM), starting with one on XML Databases and Information Retrieval which took place on 21 June 2004 at the University of Twente. We have set ourselves two goals for the workshop series: i) To provide a forum to share original ideas as well as research results on data management problems; ii) To bring together researchers from the database community and researchers from related research fields

Method and data evaluation at NASA endocrine laboratory

The biomedical data of the astronauts on Skylab 3 were analyzed to evaluate the univariate statistical methods for comparing endocrine series experiments in relation to other medical experiments. It was found that an information storage and retrieval system was needed to facilitate statistical analyses