Search (67 results, page 4 of 4)

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Abstract

The purpose of this analysis was to evaluate an existing set of search tasks in terms of their effectiveness as part of a "shared infrastructure" for conducting interactive IR research. Twenty search tasks that varied in their cognitive complexity and domain were assigned to 47 study participants; the 3,101 moves used to complete those tasks were then analyzed in terms of frequency of each type of move and the sequential patterns they formed. The cognitive complexity of the tasks influenced the number of moves used to complete the tasks, with the most complex (i.e., Create) tasks requiring more moves than tasks at other levels of complexity. Across the four domains, the Commerce tasks elicited more search moves per search. When sequences of moves were analyzed, seven patterns were identified; some of these patterns were associated with particular task characteristics. The findings suggest that search tasks can be designed to elicit particular types of search behaviors and, thus, allow researchers to focus attention on particular aspects of IR interactions.

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Abstract

Information extraction systems discover structured information in natural language text. Having information in structured form enables much richer querying and data mining than possible over the natural language text. However, information extraction is a computationally expensive task, and hence improving the efficiency of the extraction process over large text collections is of critical interest. In this paper, we focus on an especially valuable family of text collections, namely, the so-called deep-web text collections, whose contents are not crawlable and are only available via querying. Important steps for efficient information extraction over deep-web text collections (e.g., selecting the collections on which to focus the extraction effort, based on their contents; or learning which documents within these collections-and in which order-to process, based on their words and phrases) require having a representative document sample from each collection. These document samples have to be collected by querying the deep-web text collections, an expensive process that renders impractical the existing sampling approaches developed for other data scenarios. In this paper, we systematically study the space of query-based document sampling techniques for information extraction over the deep web. Specifically, we consider (i) alternative query execution schedules, which vary on how they account for the query effectiveness, and (ii) alternative document retrieval and processing schedules, which vary on how they distribute the extraction effort over documents. We report the results of the first large-scale experimental evaluation of sampling techniques for information extraction over the deep web. Our results show the merits and limitations of the alternative query execution and document retrieval and processing strategies, and provide a roadmap for addressing this critically important building block for efficient, scalable information extraction.

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Abstract

Although many studies have identified search tactics, few studies have explored tactic transitions. This study investigated the transitions of search tactics during the Web-based search process. Bringing their own 60 search tasks, 31 participants, representing the general public with different demographic characteristics, participated in the study. Data collected from search logs and verbal protocols were analyzed by applying both qualitative and quantitative methods. The findings of this study show that participants exhibited some unique Web search tactics. They overwhelmingly employed accessing and evaluating tactics; they used fewer tactics related to modifying search statements, monitoring the search process, organizing search results, and learning system features. The contributing factors behind applying most and least frequently employed search tactics are in relation to users' efforts, trust in information retrieval (IR) systems, preference, experience, and knowledge as well as limitation of the system design. A matrix of search-tactic transitions was created to show the probabilities of transitions from one tactic to another. By applying fifth-order Markov chain, the results also presented the most common search strategies representing patterns of tactic transition occurring at the beginning, middle, and ending phases within one search session. The results of this study generated detailed and useful guidance for IR system design to support the most frequently applied tactics and transitions, to reduce unnecessary transitions, and support transitions at different phases.

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Abstract

Both user involvement and system support play important roles in applying search tactics. To apply search tactics in the information retrieval (IR) processes, users make decisions and take actions in the search process, while IR systems assist them by providing different system features. After analyzing 61 participants' information searching diaries and questionnaires we identified various types of user involvement and system support in applying different types of search tactics. Based on quantitative analysis, search tactics were classified into 3 groups: user-dominated, system-dominated, and balanced tactics. We further explored types of user involvement and types of system support in applying search tactics from the 3 groups. The findings show that users and systems play major roles in applying user-dominated and system-dominated tactics, respectively. When applying balanced tactics, users and systems must collaborate closely with each other. In this article, we propose a model that illustrates user involvement and system support as they occur in user-dominated tactics, system-dominated tactics, and balanced tactics. Most important, IR system design implications are discussed to facilitate effective and efficient applications of the 3 groups of search tactics.

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