Search (31 results, page 1 of 2)

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Abstract

Purpose - This study intends to identify factors that affect relevance judgment of retrieved information as part of the 2007 TREC Legal track interactive task. Design/methodology/approach - Data were gathered and analyzed from the participants of the 2007 TREC Legal track interactive task using a questionnaire which includes not only a list of 80 relevance factors identified in prior research, but also a space for expressing their thoughts on relevance judgment in the process. Findings - This study finds that topicality remains a primary criterion, out of various options, for determining relevance, while specificity of the search request, task, or retrieved results also helps greatly in relevance judgment. Research limitations/implications - Relevance research should focus on the topicality and specificity of what is being evaluated as well as conducted in real environments. Practical implications - If multiple relevance factors are presented to assessors, the total number in a list should be below ten to take account of the limited processing capacity of human beings' short-term memory. Otherwise, the assessors might either completely ignore or inadequately consider some of the relevance factors when making judgment decisions. Originality/value - This study presents a method for reducing the artificiality of relevance research design, an apparent limitation in many related studies. Specifically, relevance judgment was made in this research as part of the 2007 TREC Legal track interactive task rather than a study devised for the sake of it. The assessors also served as searchers so that their searching experience would facilitate their subsequent relevance judgments.

Date

12. 7.2011 18:29:22

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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

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

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

Gross et al. (2015) have demonstrated that about a quarter of hits would typically be lost to keyword searchers if contemporary academic library catalogs dropped their controlled subject headings. This paper reports on an analysis of the loss levels that would result if a bibliographic database, namely the Australian Education Index (AEI), were missing the subject descriptors and identifiers assigned by its professional indexers, employing the methodology developed by Gross and Taylor (2005), and later by Gross et al. (2015). The results indicate that AEI users would lose a similar proportion of hits per query to that experienced by library catalog users: on average, 27% of the resources found by a sample of keyword queries on the AEI database would not have been found without the subject indexing, based on the Australian Thesaurus of Education Descriptors (ATED). The paper also discusses the methodological limitations of these studies, pointing out that real-life users might still find some of the resources missed by a particular query through follow-up searches, while additional resources might also be found through iterative searching on the subject vocabulary. The paper goes on to describe a new research design, based on a before - and - after experiment, which addresses some of these limitations. It is argued that this alternative design will provide a more realistic picture of the value that professionally assigned subject indexing and controlled subject vocabularies can add to literature searching of a more scholarly and thorough kind.

Content

Beitrag bei: NASKO 2017: Visualizing Knowledge Organization: Bringing Focus to Abstract Realities. The sixth North American Symposium on Knowledge Organization (NASKO 2017), June 15-16, 2017, in Champaign, IL, USA.

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Abstract

Gross et al. (2015) have demonstrated that about a quarter of hits would typically be lost to keyword searchers if contemporary academic library catalogs dropped their controlled subject headings. This article reports on an investigation of the search value that subject descriptors and identifiers assigned by professional indexers add to a bibliographic database, namely the Australian Education Index (AEI). First, a similar methodology to that developed by Gross et al. (2015) was applied, with keyword searches representing a range of educational topics run on the AEI database with and without its subject indexing. The results indicated that AEI users would also lose, on average, about a quarter of hits per query. Second, an alternative research design was applied in which an experienced literature searcher was asked to find resources on a set of educational topics on an AEI database stripped of its subject indexing and then asked to search for additional resources on the same topics after the subject indexing had been reinserted. In this study, the proportion of additional resources that would have been lost had it not been for the subject indexing was again found to be about a quarter of the total resources found for each topic, on average.

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Abstract

Modeling multidimensional relevance in information retrieval (IR) has attracted much attention in recent years. However, most existing studies are conducted through relatively small-scale user studies, which may not reflect a real-world and natural search scenario. In this article, we propose to study the multidimensional user relevance model (MURM) on large scale query logs, which record users' various search behaviors (e.g., query reformulations, clicks and dwelling time, etc.) in natural search settings. We advance an existing MURM model (including five dimensions: topicality, novelty, reliability, understandability, and scope) by providing two additional dimensions, that is, interest and habit. The two new dimensions represent personalized relevance judgment on retrieved documents. Further, for each dimension in the enriched MURM model, a set of computable features are formulated. By conducting extensive document ranking experiments on Bing's query logs and TREC session Track data, we systematically investigated the impact of each dimension on retrieval performance and gained a series of insightful findings which may bring benefits for the design of future IR systems.

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Abstract

We present the TREC Open Search track, which represents a new evaluation paradigm for information retrieval. It offers the possibility for researchers to evaluate their approaches in a live setting, with real, unsuspecting users of an existing search engine. The first edition of the track focuses on the academic search domain and features the ad-hoc scientific literature search task. We report on experiments with three different academic search engines: Cite-SeerX, SSOAR, and Microsoft Academic Search.

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Abstract

While test collections provide the cornerstone for Cranfield-based evaluation of information retrieval (IR) systems, it has become practically infeasible to rely on traditional pooling techniques to construct test collections at the scale of today's massive document collections (e.g., ClueWeb12's 700M+ Webpages). This has motivated a flurry of studies proposing more cost-effective yet reliable IR evaluation methods. In this paper, we propose a new intelligent topic selection method which reduces the number of search topics (and thereby costly human relevance judgments) needed for reliable IR evaluation. To rigorously assess our method, we integrate previously disparate lines of research on intelligent topic selection and deep vs. shallow judging (i.e., whether it is more cost-effective to collect many relevance judgments for a few topics or a few judgments for many topics). While prior work on intelligent topic selection has never been evaluated against shallow judging baselines, prior work on deep vs. shallow judging has largely argued for shallowed judging, but assuming random topic selection. We argue that for evaluating any topic selection method, ultimately one must ask whether it is actually useful to select topics, or should one simply perform shallow judging over many topics? In seeking a rigorous answer to this over-arching question, we conduct a comprehensive investigation over a set of relevant factors never previously studied together: 1) method of topic selection; 2) the effect of topic familiarity on human judging speed; and 3) how different topic generation processes (requiring varying human effort) impact (i) budget utilization and (ii) the resultant quality of judgments. Experiments on NIST TREC Robust 2003 and Robust 2004 test collections show that not only can we reliably evaluate IR systems with fewer topics, but also that: 1) when topics are intelligently selected, deep judging is often more cost-effective than shallow judging in evaluation reliability; and 2) topic familiarity and topic generation costs greatly impact the evaluation cost vs. reliability trade-off. Our findings challenge conventional wisdom in showing that deep judging is often preferable to shallow judging when topics are selected intelligently.

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Abstract

Bias quantification of retrieval functions with the help of document retrievability scores has recently evolved as an important evaluation measure for recall-oriented retrieval applications. While numerous studies have evaluated retrieval bias of retrieval functions, solid validation of its impact on realistic types of queries is still limited. This is due to the lack of well-accepted criteria for query generation for estimating retrievability. Commonly, random queries are used for approximating documents retrievability due to the prohibitively large query space and time involved in processing all queries. Additionally, a cumulative retrievability score of documents over all queries is used for analyzing retrieval functions (retrieval) bias. However, this approach does not consider the difference between different query characteristics (QCs) and their influence on retrieval functions' bias quantification. This article provides an in-depth study of retrievability over different QCs. It analyzes the correlation of lower/higher retrieval bias with different query characteristics. The presence of strong correlation between retrieval bias and query characteristics in experiments indicates the possibility of determining retrieval bias of retrieval functions without processing an exhaustive query set. Experiments are validated on TREC Chemical Retrieval Track consisting of 1.2 million patent documents.

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Abstract

This study investigates the retrieval effectiveness of collaborative tags and author keywords in different environments through controlled experiments. Three test collections were built. The first collection tests the impact of tags on retrieval performance when only the title and abstract are available (the abstract environment). The second tests the impact of tags when the full text is available (the full-text environment). The third compares the retrieval effectiveness of tags and author keywords in the abstract environment. In addition, both single-word queries and phrase queries are tested to understand the impact of different query types. Our findings suggest that including tags and author keywords in indexes can enhance recall but may improve or worsen average precision depending on retrieval environments and query types. Indexing tags and author keywords for searching using phrase queries in the abstract environment showed improved average precision, whereas indexing tags for searching using single-word queries in the full-text environment led to a significant drop in average precision. The comparison between tags and author keywords in the abstract environment indicates that they have comparable impact on average precision, but author keywords are more advantageous in enhancing recall. The findings from this study provide useful implications for designing retrieval systems that incorporate tags and author keywords.

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Abstract

Purpose - The purpose of this paper is to examine how various types of TREC data can be used to better understand relevance and serve as test-bed for exploring relevance. The author proposes that there are many interesting studies that can be performed on the TREC data collections that are not directly related to evaluating systems but to learning more about human judgements of information and relevance and that these studies can provide useful research questions for other types of investigation. Design/methodology/approach - Through several case studies the author shows how existing data from TREC can be used to learn more about the factors that may affect relevance judgements and interactive search decisions and answer new research questions for exploring relevance. Findings - The paper uncovers factors, such as familiarity, interest and strictness of relevance criteria, that affect the nature of relevance assessments within TREC, contrasting these against findings from user studies of relevance. Research limitations/implications - The research only considers certain uses of TREC data and assessment given by professional relevance assessors but motivates further exploration of the TREC data so that the research community can further exploit the effort involved in the construction of TREC test collections. Originality/value - The paper presents an original viewpoint on relevance investigations and TREC itself by motivating TREC as a source of inspiration on understanding relevance rather than purely as a source of evaluation material.

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Abstract

Numerous past studies have demonstrated the effectiveness of the relevance model (RM) for information retrieval (IR). This approach enables relevance or pseudo-relevance feedback to be incorporated within the language modeling framework of IR. In the traditional RM, the feedback information is used to improve the estimate of the query language model. In this article, we introduce an extension of RM in the setting of relevance feedback. Our method provides an additional way to incorporate feedback via the improvement of the document language models. Specifically, we make use of the context information of known relevant and nonrelevant documents to obtain weighted counts of query terms for estimating the document language models. The context information is based on the words (unigrams or bigrams) appearing within a text window centered on query terms. Experiments on several Text REtrieval Conference (TREC) collections show that our context-dependent relevance model can improve retrieval performance over the baseline RM. Together with previous studies within the BM25 framework, our current study demonstrates that the effectiveness of our method for using context information in IR is quite general and not limited to any specific retrieval model.

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Abstract

Users of web search engines are known to mostly focus on the top ranked results of the search engine result page. While many studies support this well known information seeking pattern only few studies concentrate on the question what users are missing by neglecting lower ranked results. To learn more about the relevance distributions in the so-called long tail we conducted a relevance assessment study with the Million Short long-tail web search engine. While we see a clear difference in the content between the head and the tail of the search engine result list we see no statistical significant differences in the binary relevance judgments and weak significant differences when using graded relevance. The tail contains different but still valuable results. We argue that the long tail can be a rich source for the diversification of web search engine result lists but it needs more evaluation to clearly describe the differences.

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Abstract

In this article, the use of a new term extraction method for query expansion (QE) in text retrieval is investigated. The new method expands the initial query with a structured representation made of weighted word pairs (WWP) extracted from a set of training documents (relevance feedback). Standard text retrieval systems can handle a WWP structure through custom Boolean weighted models. We experimented with both the explicit and pseudorelevance feedback schemas and compared the proposed term extraction method with others in the literature, such as KLD and RM3. Evaluations have been conducted on a number of test collections (Text REtrivel Conference [TREC]-6, -7, -8, -9, and -10). Results demonstrated that the QE method based on this new structure outperforms the baseline.

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Date

22. 7.2012 19:25:54

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Abstract

This paper reports on an investigation into the scholarly impact of the TRECVid (Text Retrieval and Evaluation Conference, Video Retrieval Evaluation) benchmarking conferences between 2003 and 2009. The contribution of TRECVid to research in video retrieval is assessed by analyzing publication content to show the development of techniques and approaches over time and by analyzing publication impact through publication numbers and citation analysis. Popular conference and journal venues for TRECVid publications are identified in terms of number of citations received. For a selection of participants at different career stages, the relative importance of TRECVid publications in terms of citations vis à vis their other publications is investigated. TRECVid, as an evaluation conference, provides data on which research teams 'scored' highly against the evaluation criteria and the relationship between 'top scoring' teams at TRECVid and the 'top scoring' papers in terms of citations is analyzed. A strong relationship was found between 'success' at TRECVid and 'success' at citations both for high scoring and low scoring teams. The implications of the study in terms of the value of TRECVid as a research activity, and the value of bibliometric analysis as a research evaluation tool, are discussed.

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Abstract

With the increasing number and diversity of search tools available, interest in the evaluation of search systems, particularly from a user perspective, has grown among researchers. More researchers are designing and evaluating interactive information retrieval (IIR) systems and beginning to innovate in evaluation methods. Maturation of a research specialty relies on the ability to replicate research, provide standards for measurement and analysis, and understand past endeavors. This article presents a historical overview of 40 years of IIR evaluation studies using the method of systematic review. A total of 2,791 journal and conference units were manually examined and 127 articles were selected for analysis in this study, based on predefined inclusion and exclusion criteria. These articles were systematically coded using features such as author, publication date, sources and references, and properties of the research method used in the articles, such as number of subjects, tasks, corpora, and measures. Results include data describing the growth of IIR studies over time, the most frequently occurring and cited authors and sources, and the most common types of corpora and measures used. An additional product of this research is a bibliography of IIR evaluation research that can be used by students, teachers, and those new to the area. To the authors' knowledge, this is the first historical, systematic characterization of the IIR evaluation literature, including the documentation of methods and measures used by researchers in this specialty.

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Abstract

A large body of research work examined, from both the query side and the user behavior side, the characteristics of medical- and health-related searches. One of the core issues in medical information retrieval (IR) is diversity of tasks that lead to diversity of categories of information needs and queries. From the evaluation perspective, another related and challenging issue is the limited availability of appropriate test collections allowing the experimental validation of medically task oriented IR techniques and systems. In this paper, we explore the peculiarities of TREC and CLEF medically oriented tasks and queries through the analysis of the differences and the similarities between queries across tasks, with respect to length, specificity, and clarity features and then study their effect on retrieval performance. We show that, even for expert oriented queries, language specificity level varies significantly across tasks as well as search difficulty. Additional findings highlight that query clarity factors are task dependent and that query terms specificity based on domain-specific terminology resources is not significantly linked to term rareness in the document collection. The lessons learned from our study could serve as starting points for the design of future task-based medical information retrieval frameworks.

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Abstract

Purpose This paper demonstrates how to apply traditional information retrieval evaluation methods based on standards from the Text REtrieval Conference (TREC) and web search evaluation to all types of modern library information systems including online public access catalogs, discovery systems, and digital libraries that provide web search features to gather information from heterogeneous sources. Design/methodology/approach We apply conventional procedures from information retrieval evaluation to the library information system context considering the specific characteristics of modern library materials. Findings We introduce a framework consisting of five parts: (1) search queries, (2) search results, (3) assessors, (4) testing, and (5) data analysis. We show how to deal with comparability problems resulting from diverse document types, e.g., electronic articles vs. printed monographs and what issues need to be considered for retrieval tests in the library context. Practical implications The framework can be used as a guideline for conducting retrieval effectiveness studies in the library context. Originality/value Although a considerable amount of research has been done on information retrieval evaluation, and standards for conducting retrieval effectiveness studies do exist, to our knowledge this is the first attempt to provide a systematic framework for evaluating the retrieval effectiveness of twenty-first-century library information systems. We demonstrate which issues must be considered and what decisions must be made by researchers prior to a retrieval test.