Search (19 results, page 1 of 1)

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Abstract

Question Answering is an area of information retrieval with the added challenge of applying sophisticated techniques to identify the complex syntactic and semantic relationships present in text in order to provide a more sophisticated and satisfactory response to the user's information needs. For this reason, the authors see question answering as the next step beyond standard information retrieval. In this chapter state of the art question answering is covered focusing on providing an overview of systems, techniques and approaches that are likely to be employed in the next generations of search engines. Special attention is paid to question answering using the World Wide Web as the data source and to question answering exploiting the possibilities of Semantic Web. Considerations about the current issues and prospects for promising future research are also provided.

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64431.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

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Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64427.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

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Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64432.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

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Abstract

Many studies have demonstrated that people engage in a variety of different information behaviors when engaging in information seeking. However, standard information retrieval systems such as Web search engines continue to be designed to support mainly one such behavior, specified searching. This situation has led to suggestions that people would be better served by information retrieval systems which support different kinds of information-seeking strategies. This article reports on an experiment comparing the retrieval effectiveness of an integrated interactive information retrieval (IIR) system which adapts to support different information-seeking strategies with that of a standard baseline IIR system. The experiment, with 32 participants each searching on eight different topics, indicates that using the integrated IIR system resulted in significantly better user satisfaction with search results, significantly more effective interaction, and significantly better usability than that using the baseline system.

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Abstract

Children represent an increasing group of web users. Some of the key problems that hamper their search experience is their limited vocabulary, their difficulty in using the right keywords, and the inappropriateness of their general-purpose query suggestions. In this work, we propose a method that uses tags from social media to suggest queries related to children's topics. Concretely, we propose a simple yet effective approach to bias a random walk defined on a bipartite graph of web resources and tags through keywords that are more commonly used to describe resources for children. We evaluate our method using a large query log sample of queries submitted by children. We show that our method outperforms by a large margin the query suggestions of modern search engines and state-of-the art query suggestions based on random walks. We improve further the quality of the ranking by combining the score of the random walk with topical and language modeling features to emphasize even more the child-related aspects of the query suggestions.

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Abstract

Query classification is an important part of exploring the characteristics of web queries. Existing studies are mainly based on Broder's classification scheme and classify user queries into navigational, informational, and transactional categories according to users' information needs. In this article, we present a novel classification scheme from the perspective of queries' temporal patterns. Queries' temporal patterns are inherent time series patterns of the search volumes of queries that reflect the evolution of the popularity of a query over time. By analyzing the temporal patterns of queries, search engines can more deeply understand the users' search intents and thus improve performance. Furthermore, we extract three groups of features based on the queries' search volume time series and use a support vector machine (SVM) to automatically detect the temporal patterns of user queries. Extensive experiments on the Million Query Track data sets of the Text REtrieval Conference (TREC) demonstrate the effectiveness of our approach.

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Abstract

Purpose - This paper aims to report findings from an exploratory study investigating the web interactions and technoliteracy of children in the early childhood years. Previous research has studied aspects of older children's technoliteracy and web searching; however, few studies have analyzed web search data from children younger than six years of age. Design/methodology/approach - The study explored the Google web searching and technoliteracy of young children who are enrolled in a "preparatory classroom" or kindergarten (the year before young children begin compulsory schooling in Queensland, Australia). Young children were video- and audio-taped while conducting Google web searches in the classroom. The data were qualitatively analysed to understand the young children's web search behaviour. Findings - The findings show that young children engage in complex web searches, including keyword searching and browsing, query formulation and reformulation, relevance judgments, successive searches, information multitasking and collaborative behaviours. The study results provide significant initial insights into young children's web searching and technoliteracy. Practical implications - The use of web search engines by young children is an important research area with implications for educators and web technologies developers. Originality/value - This is the first study of young children's interaction with a web search engine.

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Abstract

In recent years, searching the web on mobile devices has become enormously popular. Because mobile devices have relatively small screens and show fewer search results, search behavior with mobile devices may be different from that with desktops or laptops. Therefore, examining these differences may suggest better, more efficient designs for mobile search engines. In this experiment, we use eye tracking to explore user behavior and performance. We analyze web searches with 2 task types on 2 differently sized screens: one for a desktop and the other for a mobile device. In addition, we examine the relationships between search performance and several search behaviors to allow further investigation of the differences engendered by the screens. We found that users have more difficulty extracting information from search results pages on the smaller screens, although they exhibit less eye movement as a result of an infrequent use of the scroll function. However, in terms of search performance, our findings suggest that there is no significant difference between the 2 screens in time spent on search results pages and the accuracy of finding answers. This suggests several possible ideas for the presentation design of search results pages on small devices.

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Abstract

In this study, we evaluated the ability of computational cognitive models of web-navigation like CoLiDeS and CoLiDeS+ to model i) user interactions with search engines and ii) individual differences in search behavior due to variations in cognitive factors such as aging. CoLiDeS and CoLiDeS+ were extended to predict user clicks on search engine result pages. Their performance was evaluated using actual behavioral data from an experiment in which 2 types of information search tasks (simple vs. difficult), were presented to younger and older participants. The results showed that the model predictions matched significantly better with the actual user behavior on difficult tasks compared to simple tasks and with younger participants compared to older participants, especially for difficult tasks. Also, the matches were significantly better with CoLiDeS+ compared to CoLiDeS, especially for difficult tasks. We conclude that the advanced capabilities of CoLiDeS+, such as incorporating contextual information and implementing backtracking strategies enable it to predict user behavior significantly better than CoLiDeS, especially on difficult tasks. The usefulness of these modeling outcomes for the design of support systems for older adults is discussed.

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Abstract

Over the past decade, the volume of information available digitally over the Internet has grown enormously. Technical developments in the area of search, such as Google's Page Rank algorithm, have proved so good at serving relevant results that Internet search has become integrated into daily human activity. One can endlessly explore topics of interest simply by querying and reading through the resulting links. Yet, although search engines are well known for providing relevant results based on users' queries, users do not always receive the results they are looking for. Google's Director of Research describes clickstream evidence of frustrated users repeatedly reformulating queries and searching through page after page of results. Given the general quality of search engine results, one must consider the possibility that the frustrated user's query is not effective; that is, it does not describe the essence of the user's interest. Indeed, extensive research into human search behavior has found that humans are not very effective at formulating good search queries that describe what they are interested in. Ideally, the user should simply point to a portion of text that sparked the user's interest, and a system should automatically formulate a search query that captures the essence of the text. In this paper, we describe an implemented system that provides this capability. We first describe how our work differs from existing work in automatic query formulation, and propose a new method for improved quantification of the relevance of candidate search terms drawn from input text using phrase-level analysis. We then propose an implementable method designed to provide relevant queries based on a user's text input. We demonstrate the quality of our results and performance of our system through experimental studies. Our results demonstrate that our system produces relevant search terms with roughly two-thirds precision and recall compared to search terms selected by experts, and that typical users find significantly more relevant results (31% more relevant) more quickly (64% faster) using our system than self-formulated search queries. Further, we show that our implementation can scale to request loads of up to 10 requests per second within current online responsiveness expectations (<2-second response times at the highest loads tested).

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Abstract

Information searching in practice seldom is an end in itself. In work, work task (WT) performance forms the context, which information searching should serve. Therefore, information retrieval (IR) systems development/evaluation should take the WT context into account. The present paper analyzes how WT features: task complexity and task types, affect information searching in authentic work: the types of information needs, search processes, and search media. We collected data on 22 information professionals in authentic work situations in three organization types: city administration, universities, and companies. The data comprise 286 WTs and 420 search tasks (STs). The data include transaction logs, video recordings, daily questionnaires, interviews. and observation. The data were analyzed quantitatively. Even if the participants used a range of search media, most STs were simple throughout the data, and up to 42% of WTs did not include searching. WT's effects on STs are not straightforward: different WT types react differently to WT complexity. Due to the simplicity of authentic searching, the WT/ST types in interactive IR experiments should be reconsidered.

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Date

26. 1.2014 18:48:22

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Date

25. 1.2016 18:46:22

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Source

Journal of the Association for Information Science and Technology. 68(2017) no.1, S.22-35

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Date

9.12.2018 16:22:25

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Date

20. 1.2015 18:30:22

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20. 1.2015 18:30:22

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20. 1.2015 18:30:22

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Date

3. 5.1997 8:44:22