Search (132 results, page 1 of 7)

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Abstract

A study of the number of irrelevant documents retrieved in a co-ordinate indexing system that does not employ eitherr roles or links. These tests were based on one hundred actual inquiries received in the library and therefore an evaluation of recall efficiency is not included. Over half the enquiries produced no noise, but the mean average percentage niose figure was approximately 33 per cent based on a total average retireval figure of eighteen documents per search. Details of the size of the indexed collection, methods of indexing, and an analysis of the reasons for the retrieval of irrelevant documents are discussed, thereby providing information officers who are thinking of installing such a system with some evidence on which to base a decision as to whether or not to utilize these devices

Source

Journal of documentation. 22(1966), S.203-209

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Abstract

Undergraduates were tested to establish how they searched databases, the effectiveness of their searches and their satisfaction with them. The students' cognitive and learning styles were determined by the Lancaster Approaches to Studying Inventory and Riding's Cognitive Styles Analysis tests. There were significant differences in the searching behaviour and the effectiveness of the searches carried out by students with different learning and cognitive styles. Computer-assisted learning (CAL) packages were developed for three departments. The effectiveness of the packages were evaluated. Significant differences were found in the ways students with different learning styles used the packages. Based on the experience gained, guidelines for the teaching of information skills and the production and use of packages were prepared. About 2/3 of the searches had serious weaknesses, indicating a need for effective training. It appears that choice of searching strategies, search effectiveness and use of CAL packages are all affected by the cognitive and learning styles of the searcher. Therefore, students should be made aware of their own styles and, if appropriate, how to adopt more effective strategies

Source

Journal of information science. 22(1996) no.2, S.79-92

Theme

Computer Based Training

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Abstract

The majority of information retrieval experiments are evaluated by measures such as average precision and average recall. Fundamental decisions about the superiority of one retrieval technique over another are made solely on the bases of these measures. We claim that average performance figures need to be validated with a careful statistical analysis and that there is a great deal of additional information that can be uncovered by looking closely at the results of individual queries. This article is a case study of stemming algorithms which describes a number of novel approaches to evaluation and demonstrates their value

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Abstract

Purpose The purpose of this paper is to propose a method to have more accurate results in comparing performance of the paired information retrieval (IR) systems with reference to the current method, which is based on the mean effectiveness scores of the systems across a set of identified topics/queries. Design/methodology/approach Based on the proposed approach, instead of the classic method of using a set of topic scores, the documents level scores are considered as the evaluation unit. These document scores are the defined document's weight, which play the role of the mean average precision (MAP) score of the systems as a significance test's statics. The experiments were conducted using the TREC 9 Web track collection. Findings The p-values generated through the two types of significance tests, namely the Student's t-test and Mann-Whitney show that by using the document level scores as an evaluation unit, the difference between IR systems is more significant compared with utilizing topic scores. Originality/value Utilizing a suitable test collection is a primary prerequisite for IR systems comparative evaluation. However, in addition to reusable test collections, having an accurate statistical testing is a necessity for these evaluations. The findings of this study will assist IR researchers to evaluate their retrieval systems and algorithms more accurately.

Date

20. 1.2015 18:30:22

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Abstract

The performance of an information retrieval or text and media filtering system may be determined through analytic methods as well as by traditional simulation or experimental methods. These analytic methods can provide precise statements about expected performance. They can thus determine which of 2 similarly performing systems is superior. For both a single query terms and for a multiple query term retrieval model, a model for comparing the performance of different probabilistic retrieval methods is developed. This method may be used in computing the average search length for a query, given only knowledge of database parameter values. Describes predictive models for inverse document frequency, binary independence, and relevance feedback based retrieval and filtering. Simulation illustrate how the single term model performs and sample performance predictions are given for single term and multiple term problems

Date

22. 2.1996 13:14:10

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Abstract

A study was done to test the effectiveness of retrieval using title word searching. It was based on actual search profiles used in the Mechanized Information Center at Ohio State University, in order ro replicate as closely as possible actual searching conditions. Fewer than 50% of the relevant titles were retrieved by keywords in titles. The low rate of retrieval can be attributes to three sources: titles themselves, user and information specialist ignorance of the subject vocabulary in use, and to general language problems. Across fields it was found that the social sciences had the best retrieval rate, with science having the next best, and arts and humanities the lowest. Ways to enhance and supplement keyword in title searching on the computer and in printed indexes are discussed.

Date

14. 3.1996 13:22:21

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Abstract

The Initiative for the Evaluation of XML retrieval (INEX) provides a TREC-like platform for evaluating content-oriented XML retrieval systems. Since 2007, INEX has been using a set of precision-recall based metrics for its ad hoc tasks. The authors investigate the reliability and robustness of these focused retrieval measures, and of the INEX pooling method. They explore four specific questions: How reliable are the metrics when assessments are incomplete, or when query sets are small? What is the minimum pool/query-set size that can be used to reliably evaluate systems? Can the INEX collections be used to fairly evaluate "new" systems that did not participate in the pooling process? And, for a fixed amount of assessment effort, would this effort be better spent in thoroughly judging a few queries, or in judging many queries relatively superficially? The authors' findings validate properties of precision-recall-based metrics observed in document retrieval settings. Early precision measures are found to be more error-prone and less stable under incomplete judgments and small topic-set sizes. They also find that system rankings remain largely unaffected even when assessment effort is substantially (but systematically) reduced, and confirm that the INEX collections remain usable when evaluating nonparticipating systems. Finally, they observe that for a fixed amount of effort, judging shallow pools for many queries is better than judging deep pools for a smaller set of queries. However, when judging only a random sample of a pool, it is better to completely judge fewer topics than to partially judge many topics. This result confirms the effectiveness of pooling methods.

Date

22. 1.2011 14:20:56

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Abstract

Purpose The effort in addition to relevance is a major factor for satisfaction and utility of the document to the actual user. The purpose of this paper is to propose a method in generating relevance judgments that incorporate effort without human judges' involvement. Then the study determines the variation in system rankings due to low effort relevance judgment in evaluating retrieval systems at different depth of evaluation. Design/methodology/approach Effort-based relevance judgments are generated using a proposed boxplot approach for simple document features, HTML features and readability features. The boxplot approach is a simple yet repeatable approach in classifying documents' effort while ensuring outlier scores do not skew the grading of the entire set of documents. Findings The retrieval systems evaluation using low effort relevance judgments has a stronger influence on shallow depth of evaluation compared to deeper depth. It is proved that difference in the system rankings is due to low effort documents and not the number of relevant documents. Originality/value Hence, it is crucial to evaluate retrieval systems at shallow depth using low effort relevance judgments.

Date

20. 1.2015 18:30:22

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Abstract

Considers intersearcher and intrasearcher consistency in the selection of search terms. Based on an empirical study where 22 searchers from 4 different types of search environments analyzed altogether 12 search requests of 4 different types in 2 separate test situations between which 2 months elapsed. Statistically very significant differences in consistency were found according to the types of search environments and search requests. Consistency was also considered according to the extent of the scope of search concept. At level I search terms were compared character by character. At level II different search terms were accepted as the same search concept with a rather simple evaluation of linguistic expressions. At level III, in addition to level II, the hierarchical approach of the search request was also controlled. At level IV different search terms were accepted as the same search concept with a broad interpretation of the search concept. Both intersearcher and intrasearcher consistency grew most immediately after a rather simple evaluation of linguistic impressions

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Abstract

Explains briefly what constitutes the imaging process and explains how imaging can be used in information retrieval. Proposes an approach based on the concept of: 'a term is a possible world'; which enables the exploitation of term to term relationships which are estimated using an information theoretic measure. Reports results of an evaluation exercise to compare the performance of imaging retrieval, using possible world semantics, with a benchmark and using the Cranfield 2 document collection to measure precision and recall. Initially, the performance imaging retrieval was seen to be better but statistical analysis proved that the difference was not significant. The problem with imaging retrieval lies in the amount of computations needed to be performed at run time and a later experiement investigated the possibility of reducing this amount. Notes lines of further investigation

Source

Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon

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Abstract

We report data fusion experiments carried out on the four best-performing retrieval models from TREC 5. Three were conceptually/algorithmically very different from one another; one was algorithmically similar to one of the former. The objective of the test was to observe the performance of the 11 logical data fusion combinations compared to the performance of the four individual models and their intermediate fusions when following the principle of polyrepresentation. This principle is based on cognitive IR perspective (Ingwersen & Järvelin, 2005) and implies that each retrieval model is regarded as a representation of a unique interpretation of information retrieval (IR). It predicts that only fusions of very different, but equally good, IR models may outperform each constituent as well as their intermediate fusions. Two kinds of experiments were carried out. One tested restricted fusions, which entails that only the inner disjoint overlap documents between fused models are ranked. The second set of experiments was based on traditional data fusion methods. The experiments involved the 30 TREC 5 topics that contain more than 44 relevant documents. In all tests, the Borda and CombSUM scoring methods were used. Performance was measured by precision and recall, with document cutoff values (DCVs) at 100 and 15 documents, respectively. Results show that restricted fusions made of two, three, or four cognitively/algorithmically very different retrieval models perform significantly better than do the individual models at DCV100. At DCV15, however, the results of polyrepresentative fusion were less predictable. The traditional fusion method based on polyrepresentation principles demonstrates a clear picture of performance at both DCV levels and verifies the polyrepresentation predictions for data fusion in IR. Data fusion improves retrieval performance over their constituent IR models only if the models all are quite conceptually/algorithmically dissimilar and equally and well performing, in that order of importance.

Date

22. 3.2009 18:48:28

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Abstract

Purpose - One core element of interactive information retrieval (IIR) experiments is the assignment of search tasks. The purpose of this paper is to provide an analytical review of current practice in developing those search tasks to test, observe or control task complexity and difficulty. Design/methodology/approach - Over 100 prior studies of IIR were examined in terms of how each defined task complexity and/or difficulty (or related concepts) and subsequently interpreted those concepts in the development of the assigned search tasks. Findings - Search task complexity is found to include three dimensions: multiplicity of subtasks or steps, multiplicity of facets, and indeterminability. Search task difficulty is based on an interaction between the search task and the attributes of the searcher or the attributes of the search situation. The paper highlights the anomalies in our use of these two concepts, concluding with suggestions for future methodological research related to search task complexity and difficulty. Originality/value - By analyzing and synthesizing current practices, this paper provides guidance for future experiments in IIR that involve these two constructs.

Date

6. 4.2015 19:31:22

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Abstract

The University of Huddersfield, UK, is researching ways of incorporating ranking and relevance feedback techniques into a thesaurus based searching system. The INSPEC database on STN International was searched using the VUSE (View-based Search Engine) interface. Thesaurus terms from documents judged to be relevant by users were used to query INSPEC and create a ranking of documents based on probabilistic methods. An evaluation was carried out to establish whether or not it would be better for the user to continue searching with the thesaurus based front end or to use relevance feedback, looking at the ranked list of documents it would produce. Also looks at the amount of effort the user had to expend to get relevant documents in terms of the number of non relevant documents seen between relevant documents

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Abstract

Text retrieval technology targets a problem that is all too familiar: finding relevant information in large stores of electronic documents. The problem is an old one, with the first research conference devoted to the subject held in 1958 [11]. Since then the problem has continued to grow as more information is created in electronic form and more people gain electronic access. The advent of the World Wide Web, where anyone can publish so everyone must search, is a graphic illustration of the need for effective retrieval technology. The Text REtrieval Conference (TREC) is a workshop series designed to build the infrastructure necessary for the large-scale evaluation of text retrieval technology, thereby accelerating its transfer into the commercial sector. The series is sponsored by the U.S. National Institute of Standards and Technology (NIST) and the U.S. Department of Defense. At the time of this writing, there have been twelve TREC workshops and preparations for the thirteenth workshop are under way. Participants in the workshops have been drawn from the academic, commercial, and government sectors, and have included representatives from more than twenty different countries. These collective efforts have accomplished a great deal: a variety of large test collections have been built for both traditional ad hoc retrieval and related tasks such as cross-language retrieval, speech retrieval, and question answering; retrieval effectiveness has approximately doubled; and many commercial retrieval systems now contain technology first developed in TREC.

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Abstract

Discusses current information retrieval methods based on recall (R) and precision (P) for evaluating information retrieval and examines their suitability for evaluating the performance of hypermedia digital libraries. Proposes a new quantitative evaluation methodology, based on the structural analysis of hypermedia networks and the navigational and search state patterns of information seekers. Although the proposed methodology retains some of the characteristics of R and P evaluation, it could be more suitable than them for measuring the performance of information-seeking environments where information seekers can utilize arbitrary mixtures of browsing and query-based searching strategies

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Abstract

2 studies were conducted to investigate the cognitive processes involved in online document-based information retrieval. These studies led to the development of 5 computerised models of online document retrieval. These models were incorporated into a design of an 'intelligent' document-based retrieval system. Following a discussion of this system, discusses the broader implications of the research for the design of information retrieval sysems

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Abstract

Describes an intelligent agent based information retrieval model. The relevance matrix used by the intelligent agent consists of rows and columns; rows represent the documents and columns are used for keywords. Entries represent predetermined weights of keywords in documents. The search/query vector is constructed by the intelligent agent through explicit interaction with the user, using an interactive query refinement techniques. With manipulation of the relevance matrix against the search vector, the agent uses the manipulated information to filter the document representations and retrieve the most relevant documents, consequently improving the retrieval performance. Work is in progress on an experiment to compare the retrieval results from a conventional retrieval model and an intelligent agent based retrieval model. A test document collection on artificial intelligence has been selected as a sample. Retrieval tests are being carried out on a selected group of researchers using the 2 retrieval systems. Results will be compared to assess the retrieval performance using precision and recall matrices