Search (13192 results, page 3 of 660)

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Abstract

This paper presents a study of relevance feedback in a cross-language information retrieval environment. We have performed an experiment in which Portuguese speakers are asked to judge the relevance of English documents; documents hand-translated to Portuguese and documents automatically translated to Portuguese. The goals of the experiment were to answer two questions (i) how well can native Portuguese searchers recognise relevant documents written in English, compared to documents that are hand translated and automatically translated to Portuguese; and (ii) what is the impact of misjudged documents on the performance improvement that can be achieved by relevance feedback. Surprisingly, the results show that machine translation is as effective as hand translation in aiding users to assess relevance in the experiment. In addition, the impact of misjudged documents on the performance of RF is overall just moderate, and varies greatly for different query topics.

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Abstract

Since millions seek health information online, it is vital for this information to be comprehensible. Most studies use readability formulas, which ignore vocabulary, and conclude that online health information is too difficult. We developed a vocabularly-based, naïve Bayes classifier to distinguish between three difficulty levels in text. It proved 98% accurate in a 250-document evaluation. We compared our classifier with readability formulas for 90 new documents with different origins and asked representative human evaluators, an expert and a consumer, to judge each document. Average readability grade levels for educational and commercial pages was 10th grade or higher, too difficult according to current literature. In contrast, the classifier showed that 70-90% of these pages were written at an intermediate, appropriate level indicating that vocabulary usage is frequently appropriate in text considered too difficult by readability formula evaluations. The expert considered the pages more difficult for a consumer than the consumer did.

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Abstract

Users intuitively know which documents are relevant when they see them. Formal relevance assessment, however, is a complex issue. In this entry relevance assessment are described both from a human perspective and a systems perspective. Humans judge relevance in terms of the relation between the documents retrieved and the way in which these documents are understood and used. This is a subjective and personal judgment and is called user relevance. Systems compute a function between the query and the document features that the systems builders believe will cause documents to be ranked by the likelihood that a user will find the documents relevant. This is an objective measurement of relevance in terms of relations between the query and the documents retrieved-this is called system relevance (or sometimes similarity).

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Abstract

Wikipedia is frequently viewed as an inclusive medium. But inclusivity within this online encyclopedia is not a simple matter of just allowing anyone to contribute. In its quest for legitimacy as an encyclopedia, Wikipedia relies on outsiders to judge claims championed by rival editors. In choosing these experts, Wikipedians define the boundaries of acceptable comment on any given subject. Inclusivity then becomes a matter of how the boundaries of expertise are drawn. In this article I examine the nature of these boundaries and the implications they have for inclusivity and credibility as revealed through the talk pages produced and sources used by a particular subset of Wikipedia's creators-those involved in writing articles on the topic of Philippine history.

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Abstract

In the age of mobile commerce, users receive floods of commercial messages. How do users judge the relevance of such information? Is their relevance judgment affected by contextual factors, such as location and time? How do message content and contextual factors affect users' privacy concerns? With a focus on mobile ads, we propose a research model based on theories of relevance judgment and mobile marketing research. We suggest topicality, reliability, and economic value as key content factors and location and time as key contextual factors. We found mobile relevance judgment is affected mainly by content factors, whereas privacy concerns are affected by both content and contextual factors. Moreover, topicality and economic value have a synergetic effect that makes a message more relevant. Higher topicality and location precision exacerbate privacy concerns, whereas message reliability alleviates privacy concerns caused by location precision. These findings reveal an interesting intricacy in user relevance judgment and privacy concerns and provide nuanced guidance for the design and delivery of mobile commercial information.

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Abstract

This research revisits the classic Turing test and compares recent large language models such as ChatGPT for their abilities to reproduce human-level comprehension and compelling text generation. Two task challenges- summary and question answering- prompt ChatGPT to produce original content (98-99%) from a single text entry and sequential questions initially posed by Turing in 1950. We score the original and generated content against the OpenAI GPT-2 Output Detector from 2019, and establish multiple cases where the generated content proves original and undetectable (98%). The question of a machine fooling a human judge recedes in this work relative to the question of "how would one prove it?" The original contribution of the work presents a metric and simple grammatical set for understanding the writing mechanics of chatbots in evaluating their readability and statistical clarity, engagement, delivery, overall quality, and plagiarism risks. While Turing's original prose scores at least 14% below the machine-generated output, whether an algorithm displays hints of Turing's true initial thoughts (the "Lovelace 2.0" test) remains unanswerable.

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BK

06.00 / Information und Dokumentation: Allgemeines

Classification

06.00 / Information und Dokumentation: Allgemeines

Footnote

Rez. in: JASIST 58(2007) no.2, S.302 (L.A. Ennis):"This is an important and timely anthology of articles "on the normative issues surrounding information control" (p. 11). Using an interdisciplinary approach, Moore's work takes a broad look at the relatively new field of information ethics. Covering a variety of disciplines including applied ethics, intellectual property, privacy, free speech, and more, the book provides information professionals of all kinds with a valuable and thought-provoking resource. Information Ethics is divided into five parts and twenty chapters or articles. At the end of each of the five parts, the editor has included a few "discussion cases," which allows the users to apply what they just read to potential real life examples. Part I, "An Ethical Framework for Analysis," provides readers with an introduction to reasoning and ethics. This complex and philosophical section of the book contains five articles and four discussion cases. All five of the articles are really thought provoking and challenging writings on morality. For instance, in the first article, "Introduction to Moral Reasoning," Tom Regan examines how not to answer a moral question. For example, he thinks using what the majority believes as a means of determining what is and is not moral is flawed. "The Metaphysics of Morals" by Immanuel Kant looks at the reasons behind actions. According to Kant, to be moral one has to do the right thing for the right reasons. By including materials that force the reader to think more broadly and deeply about what is right and wrong, Moore has provided an important foundation and backdrop for the rest of the book. Part II, "Intellectual Property: Moral and Legal Concerns," contains five articles and three discussion cases for tackling issues like ownership, patents, copyright, and biopiracy. This section takes a probing look at intellectual and intangible property from a variety of viewpoints. For instance, in "Intellectual Property is Still Property," Judge Frank Easterbrook argues that intellectual property is no different than physical property and should not be treated any differently by law. Tom Palmer's article, "Are Patents and Copyrights Morally Justified," however, uses historical examples to show how intellectual and physical properties differ.

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Abstract

The dichotomous bipolar approach to relevance has produced an abundance of information retrieval (M) research. However, relevance studies that include consideration of users' partial relevance judgments are moving to a greater relevance clarity and congruity to impact the design of more effective [R systems. The study reported in this paper investigates the various regions of across a distribution of users' relevance judgments, including how these regions may be categorized, measured, and evaluated. An instrument was designed using four scales for collecting, measuring, and describing enduser relevance judgments. The instrument was administered to 21 end-users who conducted searches on their own information problems and made relevance judgments on a total of 1059 retrieved items. Findings include: (1) overlapping regions of relevance were found to impact the usefulness of precision ratios as a measure of IR system effectiveness, (2) both positive and negative levels of relevance are important to users as they make relevance judgments, (3) topicality was used more to reject rather than accept items as highly relevant, (4) utility was more used to judge items highly relevant, and (5) the nature of relevance judgment distribution suggested a new IR evaluation measure-median effect. Findings suggest that the middle region of a distribution of relevance judgments, also called "partial relevance," represents a key avenue for ongoing study. The findings provide implications for relevance theory, and the evaluation of IR systems

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Abstract

Subject content analysis of Web query terms is essential to understand Web searching interests. Such analysis includes exploring search topics and observing changes in their frequency distributions with time. To provide a basis for in-depth analysis of users' search interests on a larger scale, this article presents a query categorization approach to automatically classifying Web query terms into broad subject categories. Because a query is short in length and simple in structure, its intended subject(s) of search is difficult to judge. Our approach, therefore, combines the search processes of real-world search engines to obtain highly ranked Web documents based on each unknown query term. These documents are used to extract cooccurring terms and to create a feature set. An effective ranking function has also been developed to find the most appropriate categories. Three search engine logs in Taiwan were collected and tested. They contained over 5 million queries from different periods of time. The achieved performance is quite encouraging compared with that of human categorization. The experimental results demonstrate that the approach is efficient in dealing with large numbers of queries and adaptable to the dynamic Web environment. Through good integration of human and machine efforts, the frequency distributions of subject categories in response to changes in users' search interests can be systematically observed in real time. The approach has also shown potential for use in various information retrieval applications, and provides a basis for further Web searching studies.

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Abstract

This research creates an intelligent agent for automated collection development in a digital library setting. It uses a predictive model based an facets of each Web page to select scholarly works. The criteria came from the academic library selection literature, and a Delphi study was used to refine the list to 41 criteria. A Perl program was designed to analyze a Web page for each criterion and applied to a large collection of scholarly and nonscholarly Web pages. Bibliomining, or data mining for libraries, was then used to create different classification models. Four techniques were used: logistic regression, nonparametric discriminant analysis, classification trees, and neural networks. Accuracy and return were used to judge the effectiveness of each model an test datasets. In addition, a set of problematic pages that were difficult to classify because of their similarity to scholarly research was gathered and classified using the models. The resulting models could be used in the selection process to automatically create a digital library of Webbased scholarly research works. In addition, the technique can be extended to create a digital library of any type of structured electronic information.

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Abstract

When bibliometric data are converted to term frequency (tf) and inverse document frequency (idf) values, plotted as pennant diagrams, and interpreted according to Sperber and Wilson's relevance theory (RT), the results evoke major variables of information science (IS). These include topicality, in the sense of intercohesion and intercoherence among texts; cognitive effects of texts in response to people's questions; people's levels of expertise as a precondition for cognitive effects; processing effort as textual or other messages are received; specificity of terms as it affects processing effort; relevance, defined in RT as the effects/effort ratio; and authority of texts and their authors. While such concerns figure automatically in dialogues between people, they become problematic when people create or use or judge literature-based information systems. The difficulty of achieving worthwhile cognitive effects and acceptable processing effort in human-system dialogues explains why relevance is the central concern of IS. Moreover, since relevant communication with both systems and unfamiliar people is uncertain, speakers tend to seek cognitive effects that cost them the least effort. Yet hearers need greater effort, often greater specificity, from speakers if their responses are to be highly relevant in their turn. This theme of mismatch manifests itself in vague reference questions, underdeveloped online searches, uncreative judging in retrieval evaluation trials, and perfunctory indexing. Another effect of least effort is a bias toward topical relevance over other kinds. RT can explain these outcomes as well as more adaptive ones. Pennant diagrams, applied here to a literature search and a Bradford-style journal analysis, can model them. Given RT and the right context, bibliometrics may predict psychometrics.

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Abstract

Previous research assessing the effectiveness of structured abstracts has been limited in two respects. First, when comparing structured abstracts with traditional ones, investigators usually have rewritten the original abstracts, and thus confounded changes in the layout with changes in both the wording and the content of the text. Second, investigators have not always included the title of the article together with the abstract when asking participants to judge the quality of the abstracts, yet titles alert readers to the meaning of the materials that follow. The aim of this research was to redress these limitations. Three studies were carried out. Four versions of each of four abstracts were prepared. These versions consisted of structured/traditional abstracts matched in content, with and without titles. In Study 1, 64 undergraduates each rated one of these abstracts on six separate rating scales. In Study 2, 225 academics and research workers rated the abstracts electronically, and in Study 3, 252 information scientists did likewise. In Studies 1 and 3, the respondents rated the structured abstracts significantly more favorably than they did the traditional ones, but the presence or absence of titles had no effect on their judgments. In Study 2, no main effects were observed for structure or for titles. The layout of the text, together with the subheadings, contributed to the higher ratings of effectiveness for structured abstracts, but the presence or absence of titles had no clear effects in these experimental studies. It is likely that this spatial organization, together with the greater amount of information normally provided in structured abstracts, explains why structured abstracts are generally judged to be superior to traditional ones.

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Abstract

The information science research community is characterized by a paradigm split, with a system-centered cluster working on information retrieval (IR) algorithms and a user-centered cluster working on user behavior. The two clusters rarely leverage each other's insight and strength. One major suggestion from user-centered studies is to treat the relevance judgment of documents as a subjective, multidimensional, and dynamic concept rather than treating it as objective and based on topicality only. This study explores the possibility to enhance users' topicality-based relevance judgment with subjective novelty judgment in interactive IR. A set of systems is developed which differs in the way the novelty judgment is incorporated. In particular, this study compares systems which assume that users' novelty judgment is directed to a certain subtopic area and those which assume that users' novelty judgment is undirected. This study also compares systems which assume that users judge a document based on topicality first and then novelty in a stepwise, noncompensatory fashion and those which assume that users consider topicality and novelty simultaneously and as compensatory to each other. The user study shows that systems assuming directed novelty in general have higher relevance precision, but systems assuming a stepwise judgment process and systems assuming a compensatory judgment process are not significantly different.

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Abstract

Many writers of structured abstracts spend a good deal of time revising and polishing their texts - but is it worth it? Do readers notice the difference? In this paper we report three studies of readers using rating scales to judge (electronically) the clarity of an original and a revised abstract, both as a whole and in its constituent parts. In Study 1, with approximately 250 academics and research workers, we found some significant differences in favor of the revised abstract, but in Study 2, with approximately 210 information scientists, we found no significant effects. Pooling the data from Studies 1 and 2, however, in Study 3, led to significant differences at a higher probability level between the perception of the original and revised abstract as a whole and between the same components as found in Study 1. These results thus indicate that the revised abstract as a whole, as well as certain specific components of it, were judged significantly clearer than the original one. In short, the results of these experiments show that readers can and do perceive differences between original and revised texts - sometimes - and that therefore these efforts are worth the time and effort.

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Abstract

Project risks encompass both internal and external factors that are interrelated, influencing others in a causal way. It is very important to identify those factors and their causal relationships to reduce the project risk. In the past, most IT companies evaluate project risk by roughly measuring the related factors, but ignoring the important fact that there are complicated causal relationships among them. There is a strong need to develop more effective mechanisms to systematically judge all factors related to project risk and identify the causal relationships among those factors. To accomplish this research objective, our study adopts a cognitive map (CM)-based mechanism called the MACOM (Multi-Agents COgnitive Map), where CM is represented by a set of multi-agents, each embedded with basic intelligence to determine its causal relationships with other agents. CM has proven especially useful in solving unstructured problems with many variables and causal relationships; however, simply applying CM to project risk management is not enough because most causal relationships are hard to identify and measure exactly. To overcome this problem, we have borrowed a multi-agent metaphor in which CM is represented by a set of multi-agents, and project risk is explained through the interaction of the multi-agents. Such an approach presents a new computational capability for resolving complicated decision problems. Using the MACOM framework, we have proved that the task of resolving the IS project risk management could be systematically and intelligently solved, and in this way, IS project managers can be given robust decision support.

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Abstract

Purpose - The purpose of this paper is to resolve a gap in the knowledge of how people with dyslexia interact with information retrieval (IR) systems, specifically an understanding of their information-searching behaviour. Design/methodology/approach - The dyslexia cognitive profile is used to design a logging system, recording the difference between two sets of participants: dyslexic and control users. A standard Okapi interface is used - together with two standard TREC topics - in order to record the information searching behaviour of these users. Findings - Using the log data, the differences in information-searching behaviour of control and dyslexic users, i.e. in the way the two groups interact with Okapi, are established and it also established that qualitative information collected (such as experience etc.) may not be able to account for these differences. Evidence from query variables was unable to distinguish between groups, but differences on topic for the same variables were recorded. Users who view more documents tended to judge more documents as being relevant, in terms of either the user group or topic. Session data indicated that there may be an important difference between the number of iterations used in a search between the user groups, as there may be little effect from the topic on this variable. Originality/value - This is the first study of the effect of dyslexia on information search behaviour, and it provides some evidence to take the field forward.

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Abstract

In real-world information retrieval systems, the underlying document collection is rarely stable or definitive. This work is focused on the study of signals extracted from the content of documents at different points in time for the purpose of weighting individual terms in a document. The basic idea behind our proposals is that terms that have existed for a longer time in a document should have a greater weight. We propose 4 term weighting functions that use each document's history to estimate a current term score. To evaluate this thesis, we conduct 3 independent experiments using a collection of documents sampled from Wikipedia. In the first experiment, we use data from Wikipedia to judge each set of terms. In a second experiment, we use an external collection of tags from a popular social bookmarking service as a gold standard. In the third experiment, we crowdsource user judgments to collect feedback on term preference. Across all experiments results consistently support our thesis. We show that temporally aware measures, specifically the proposed revision term frequency and revision term frequency span, outperform a term-weighting measure based on raw term frequency alone.

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Abstract

Expertise retrieval has attracted significant interest in the field of information retrieval. Expert finding has been studied extensively, with less attention going to the complementary task of expert profiling, that is, automatically identifying topics about which a person is knowledgeable. We describe a test collection for expert profiling in which expert users have self-selected their knowledge areas. Motivated by the sparseness of this set of knowledge areas, we report on an assessment experiment in which academic experts judge a profile that has been automatically generated by state-of-the-art expert-profiling algorithms; optionally, experts can indicate a level of expertise for relevant areas. Experts may also give feedback on the quality of the system-generated knowledge areas. We report on a content analysis of these comments and gain insights into what aspects of profiles matter to experts. We provide an error analysis of the system-generated profiles, identifying factors that help explain why certain experts may be harder to profile than others. We also analyze the impact on evaluating expert-profiling systems of using self-selected versus judged system-generated knowledge areas as ground truth; they rank systems somewhat differently but detect about the same amount of pairwise significant differences despite the fact that the judged system-generated assessments are more sparse.

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Abstract

Purpose - The purpose of this paper is to examine the effect of the Helping Interdisciplinary Vocabulary Engineering (HIVE) system on the inter-indexer consistency of information professionals when assigning keywords to a scientific abstract. This study examined first, the inter-indexer consistency of potential HIVE users; second, the impact HIVE had on consistency; and third, challenges associated with using HIVE. Design/methodology/approach - A within-subjects quasi-experimental research design was used for this study. Data were collected using a task-scenario based questionnaire. Analysis was performed on consistency results using Hooper's and Rolling's inter-indexer consistency measures. A series of t-tests was used to judge the significance between consistency measure results. Findings - Results suggest that HIVE improves inter-indexing consistency. Working with HIVE increased consistency rates by 22 percent (Rolling's) and 25 percent (Hooper's) when selecting relevant terms from all vocabularies. A statistically significant difference exists between the assignment of free-text keywords and machine-aided keywords. Issues with homographs, disambiguation, vocabulary choice, and document structure were all identified as potential challenges. Research limitations/implications - Research limitations for this study can be found in the small number of vocabularies used for the study. Future research will include implementing HIVE into the Dryad Repository and studying its application in a repository system. Originality/value - This paper showcases several features used in HIVE system. By using traditional consistency measures to evaluate a semantic web technology, this paper emphasizes the link between traditional indexing and next generation machine-aided indexing (MAI) tools.

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Abstract

Bias in peer review entails systematic prejudice that prevents accurate and objective assessment of scientific studies. The disparity between referees' opinions on the same paper typically makes it difficult to judge the paper's quality. This article presents a comprehensive study of peer review biases with regard to 2 aspects of referees: the static profiles (factual authority and self-reported confidence) and the dynamic behavioral context (the temporal ordering of reviews by a single reviewer), exploiting anonymized, real-world review reports of 2 different international conferences in information systems / computer science. Our work extends conventional bias research by considering multiple biases occurring simultaneously. Our findings show that the referees' static profiles are more dominant in peer review bias when compared to their dynamic behavioral context. Of the static profiles, self-reported confidence improved both conference fitness and impact-based bias reductions, while factual authority could only contribute to conference fitness-based bias reduction. Our results also clearly show that the reliability of referees' judgments varies along their static profiles and is contingent on the temporal interval between 2 consecutive reviews.