Search (8 results, page 1 of 1)

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Abstract

The nature of the task that leads a person to engage in information interaction, as well as of information seeking and searching tasks, have been shown to influence individuals' information behavior. Classifying tasks in a domain has been viewed as a departure point of studies on the relationship between tasks and human information behavior. However, previous task classification schemes either classify tasks with respect to the requirements of specific studies or merely classify a certain category of task. Such approaches do not lead to a holistic picture of task since a task involves different aspects. Therefore, the present study aims to develop a faceted classification of task, which can incorporate work tasks and information search tasks into the same classification scheme and characterize tasks in such a way as to help people make predictions of information behavior. For this purpose, previous task classification schemes and their underlying facets are reviewed and discussed. Analysis identifies essential facets and categorizes them into Generic facets of task and Common attributes of task. Generic facets of task include Source of task, Task doer, Time, Action, Product, and Goal. Common attributes of task includes Task characteristics and User's perception of task. Corresponding sub-facets and values are identified as well. In this fashion, a faceted classification of task is established which could be used to describe users' work tasks and information search tasks. This faceted classification provides a framework to further explore the relationships among work tasks, search tasks, and interactive information retrieval and advance adaptive IR systems design.

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Abstract

This article studies the impact of differences in citation practices at the subfield, or Web of Science subject category level, using the model introduced in Crespo, Li, and Ruiz-Castillo (2013a), according to which the number of citations received by an article depends on its underlying scientific influence and the field to which it belongs. We use the same Thomson Reuters data set of about 4.4 million articles used in Crespo et al. (2013a) to analyze 22 broad fields. The main results are the following: First, when the classification system goes from 22 fields to 219 subfields the effect on citation inequality of differences in citation practices increases from ?14% at the field level to 18% at the subfield level. Second, we estimate a set of exchange rates (ERs) over a wide [660, 978] citation quantile interval to express the citation counts of articles into the equivalent counts in the all-sciences case. In the fractional case, for example, we find that in 187 of 219 subfields the ERs are reliable in the sense that the coefficient of variation is smaller than or equal to 0.10. Third, in the fractional case the normalization of the raw data using the ERs (or subfield mean citations) as normalization factors reduces the importance of the differences in citation practices from 18% to 3.8% (3.4%) of overall citation inequality. Fourth, the results in the fractional case are essentially replicated when we adopt a multiplicative approach.

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Abstract

We study several machine learning algorithms for cross-language patent retrieval and classification. In comparison with most of other studies involving machine learning for cross-language information retrieval, which basically used learning techniques for monolingual sub-tasks, our learning algorithms exploit the bilingual training documents and learn a semantic representation from them. We study Japanese-English cross-language patent retrieval using Kernel Canonical Correlation Analysis (KCCA), a method of correlating linear relationships between two variables in kernel defined feature spaces. The results are quite encouraging and are significantly better than those obtained by other state of the art methods. We also investigate learning algorithms for cross-language document classification. The learning algorithm are based on KCCA and Support Vector Machines (SVM). In particular, we study two ways of combining the KCCA and SVM and found that one particular combination called SVM_2k achieved better results than other learning algorithms for either bilingual or monolingual test documents.

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Abstract

In this article, the authors report a series of evaluations of two metadata schemes developed for Moving Image Collections (MIC), an integrated online catalog of moving images. Through two online surveys and one experiment spanning various stages of metadata implementation, the MIC evaluation team explored a user-centered approach in which the four generic user tasks suggested by IFLA FRBR (International Association of Library Associations Functional Requirement for Bibliographic Records) were embedded in data collection and analyses. Diverse groups of users rated usefulness of individual metadata fields for finding, identifying, selecting, and obtaining moving images. The results demonstrate a consistency across these evaluations with respect to (a) identification of a set of useful metadata fields highly rated by target users for each of the FRBR generic tasks, and (b) indication of a significant interaction between MIC metadata fields and the FRBR generic tasks. The findings provide timely feedback for the MIC implementation specifically, and valuable suggestions to other similar metadata application settings in general. They also suggest the feasibility of using the four IFLA FRBR generic tasks as a framework for user-centered functional metadata evaluations.

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Abstract

To provide a basis for making predictions of the characteristics of search task (ST), based on work task (WT), and to explore the nature of WT and ST, this study examines the relationships between WT and ST (inter-relationships) and the relationships between the different facets of both WT and ST (intra-relationships), respectively. A faceted classification of task was used to conceptualize work task and search task. Twenty-four pairs of work tasks and their associated search tasks were collected, by semistructured interviews, and classified based on the classification. The results indicate that work task shapes different facets or sub-facets of its associated search tasks to different degrees. Several sub-facets of search task, such as Time (Length), Objective task complexity, and Subjective task complexity, are most strongly affected by work task. The results demonstrate that it is necessary to consider difficulty and complexity as different constructs when investigating their influence on information search behavior. The exploration of intra-relationships illustrates the difference of work task and search task in their nature. The findings provide empirical evidence to support the view that work task and search task are multi-faceted variables and their different effects on users' information search behavior should be examined.

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Source

Cataloging and classification quarterly. 38(2004) no.2, S.17-xx

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Abstract

This study explores the relationships between work task and interactive information search behavior. Work task was conceptualized based on a faceted classification of task. An experiment was conducted with six work-task types and simulated work-task situations assigned to 24 participants. The results indicate that users present different behavior patterns to approach useful information for different work tasks: They select information systems to search based on the work tasks at hand, different work tasks motivate different types of search tasks, and different facets controlled in the study play different roles in shaping users' interactive information search behavior. The results provide empirical evidence to support the view that work tasks and search tasks play different roles in a user's interaction with information systems and that work task should be considered as a multifaceted variable. The findings provide a possibility to make predictions of a user's information search behavior from his or her work task, and vice versa. Thus, this study sheds light on task-based information seeking and search, and has implications in adaptive information retrieval (IR) and personalization of IR.

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Abstract

Social media is frequently used as a platform for the exchange of information and opinions as well as propaganda dissemination. But online content can be misused for the distribution of illicit information, such as violent postings in web forums. Illicit content is highly distributed in social media, while non-illicit content is unspecific and topically diverse. It is costly and time consuming to label a large amount of illicit content (positive examples) and non-illicit content (negative examples) to train classification systems. Nevertheless, it is relatively easy to obtain large volumes of unlabeled content in social media. In this article, an artificial immune system-based technique is presented to address the difficulties in the illicit content identification in social media. Inspired by the positive selection principle in the immune system, we designed a novel labeling heuristic based on partially supervised learning to extract high-quality positive and negative examples from unlabeled datasets. The empirical evaluation results from two large hate group web forums suggest that our proposed approach generally outperforms the benchmark techniques and exhibits more stable performance.