Search (785 results, page 2 of 40)

  • × language_ss:"e"
  • × type_ss:"a"
  • × year_i:[2020 TO 2030}
  1. Das, S.; Paik, J.H.: Gender tagging of named entities using retrieval-assisted multi-context aggregation : an unsupervised approach (2023) 0.02 
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    Date
    22. 3.2023 12:00:14
    Language
    e
  2. Zhang, X.; Wang, D.; Tang, Y.; Xiao, Q.: How question type influences knowledge withholding in social Q&A community (2023) 0.02 
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    Date
    22. 9.2023 13:51:47
    Language
    e
  3. Cerda-Cosme, R.; Méndez, E.: Analysis of shared research data in Spanish scientific papers about COVID-19 : a first approach (2023) 0.02 
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    Abstract
    During the coronavirus pandemic, changes in the way science is done and shared occurred, which motivates meta-research to help understand science communication in crises and improve its effectiveness. The objective is to study how many Spanish scientific papers on COVID-19 published during 2020 share their research data. Qualitative and descriptive study applying nine attributes: (a) availability, (b) accessibility, (c) format, (d) licensing, (e) linkage, (f) funding, (g) editorial policy, (h) content, and (i) statistics. We analyzed 1,340 papers, 1,173 (87.5%) did not have research data. A total of 12.5% share their research data of which 2.1% share their data in repositories, 5% share their data through a simple request, 0.2% do not have permission to share their data, and 5.2% share their data as supplementary material. There is a small percentage that shares their research data; however, it demonstrates the researchers' poor knowledge on how to properly share their research data and their lack of knowledge on what is research data.
    Date
    21. 3.2023 19:22:02
    Language
    e
  4. Bergman, O.; Israeli, T.; Whittaker, S.: Factors hindering shared files retrieval (2020) 0.02 
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    Abstract
    Purpose Personal information management (PIM) is an activity in which people store information items in order to retrieve them later. The purpose of this paper is to test and quantify the effect of factors related to collection size, file properties and workload on file retrieval success and efficiency. Design/methodology/approach In the study, 289 participants retrieved 1,557 of their shared files in a naturalistic setting. The study used specially developed software designed to collect shared files' names and present them as targets for the retrieval task. The dependent variables were retrieval success, retrieval time and misstep/s. Findings Various factors compromise shared files retrieval including: collection size (large number of files), file properties (multiple versions, size of team sharing the file, time since most recent retrieval and folder depth) and workload (daily e-mails sent and received). The authors discuss theoretical reasons for these negative effects and suggest possible ways to overcome them. Originality/value Retrieval is the main reason people manage personal information. It is essential for retrieval to be successful and efficient, as information cannot be used unless it can be re-accessed. Prior PIM research has assumed that factors related to collection size, file properties and workload affect file retrieval. However, this is the first study to systematically quantify the negative effects of these factors. As each of these factors is expected to be exacerbated in the future, this study is a necessary first step toward addressing these problems.
    Date
    20. 1.2015 18:30:22
    Language
    e
  5. Yu, C.; Xue, H.; An, L.; Li, G.: ¬A lightweight semantic-enhanced interactive network for efficient short-text matching (2023) 0.02 
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    Abstract
    Knowledge-enhanced short-text matching has been a significant task attracting much attention in recent years. However, the existing approaches cannot effectively balance effect and efficiency. Effective models usually consist of complex network structures leading to slow inference speed and the difficulties of applications in actual practice. In addition, most knowledge-enhanced models try to link the mentions in the text to the entities of the knowledge graphs-the difficulties of entity linking decrease the generalizability among different datasets. To address these problems, we propose a lightweight Semantic-Enhanced Interactive Network (SEIN) model for efficient short-text matching. Unlike most current research, SEIN employs an unsupervised method to select WordNet's most appropriate paraphrase description as the external semantic knowledge. It focuses on integrating semantic information and interactive information of text while simplifying the structure of other modules. We conduct intensive experiments on four real-world datasets, that is, Quora, Twitter-URL, SciTail, and SICK-E. Compared with state-of-the-art methods, SEIN achieves the best performance on most datasets. The experimental results proved that introducing external knowledge could effectively improve the performance of the short-text matching models. The research sheds light on the role of lightweight models in leveraging external knowledge to improve the effect of short-text matching.
    Date
    22. 1.2023 19:05:27
    Language
    e
  6. Thelwall, M.; Kousha, K.; Abdoli, M.; Stuart, E.; Makita, M.; Wilson, P.; Levitt, J.: Why are coauthored academic articles more cited : higher quality or larger audience? (2023) 0.02 
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    Date
    22. 6.2023 18:11:50
    Language
    e
  7. Wu, Z.; Li, R.; Zhou, Z.; Guo, J.; Jiang, J.; Su, X.: ¬A user sensitive subject protection approach for book search service (2020) 0.02 
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    Date
    6. 1.2020 17:22:25
    Language
    e
  8. Marcondes, C.H.: Towards a vocabulary to implement culturally relevant relationships between digital collections in heritage institutions (2020) 0.02 
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    Date
    4. 3.2020 14:22:41
    Language
    e
  9. Yang, F.; Zhang, X.: Focal fields in literature on the information divide : the USA, China, UK and India (2020) 0.02 
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    Date
    13. 2.2020 18:22:13
    Language
    e
  10. Hartel, J.: ¬The red thread of information (2020) 0.02 
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    Date
    30. 4.2020 21:03:22
    Language
    e
  11. Ekstrand, M.D.; Wright, K.L.; Pera, M.S.: Enhancing classroom instruction with online news (2020) 0.02 
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    Date
    20. 1.2015 18:30:22
    Language
    e
  12. Kim, J.(im); Kim, J.(enna): Effect of forename string on author name disambiguation (2020) 0.02 
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    Date
    11. 7.2020 13:22:58
    Language
    e
  13. Rha, E.Y.; Belkin, N.: Exploring social aspects of task perception using cognitive sociology : a social cognitive perspective (2020) 0.02 
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    Date
    20. 1.2015 18:30:22
    Language
    e
  14. Kang, M.: Dual paths to continuous online knowledge sharing : a repetitive behavior perspective (2020) 0.02 
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    Date
    20. 1.2015 18:30:22
    Language
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  15. Chi, Y.; He, D.; Jeng, W.: Laypeople's source selection in online health information-seeking process (2020) 0.02 
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