Search (3 results, page 1 of 1)

  • × author_ss:"Liu, J."
  • × language_ss:"e"
  • × year_i:[2020 TO 2030}
  1. Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.05 
    0.048165213 = product of:
  0.07224782 = sum of:
    0.055053383 = weight(_text_:wide in 1012) [ClassicSimilarity], result of:
      0.055053383 = score(doc=1012,freq=2.0), product of:
        0.22492146 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.050763648 = queryNorm
        0.24476713 = fieldWeight in 1012, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1012)
    0.017194435 = product of:
      0.03438887 = sum of:
        0.03438887 = weight(_text_:22 in 1012) [ClassicSimilarity], result of:
          0.03438887 = score(doc=1012,freq=2.0), product of:
            0.17776565 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050763648 = queryNorm
            0.19345059 = fieldWeight in 1012, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1012)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end-to-end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro-avgs of , , and on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models.
    Date
    22. 6.2023 14:55:20
  2. Zhang, Y.; Liu, J.; Song, S.: ¬The design and evaluation of a nudge-based interface to facilitate consumers' evaluation of online health information credibility (2023) 0.02 
    0.021418769 = product of:
  0.0642563 = sum of:
    0.0642563 = sum of:
      0.029867431 = weight(_text_:web in 993) [ClassicSimilarity], result of:
        0.029867431 = score(doc=993,freq=2.0), product of:
          0.1656677 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.050763648 = queryNorm
          0.18028519 = fieldWeight in 993, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.0390625 = fieldNorm(doc=993)
      0.03438887 = weight(_text_:22 in 993) [ClassicSimilarity], result of:
        0.03438887 = score(doc=993,freq=2.0), product of:
          0.17776565 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.050763648 = queryNorm
          0.19345059 = fieldWeight in 993, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=993)
  0.33333334 = coord(1/3)
    Abstract
    Evaluating the quality of online health information (OHI) is a major challenge facing consumers. We designed PageGraph, an interface that displays quality indicators and associated values for a webpage, based on credibility evaluation models, the nudge theory, and existing empirical research concerning professionals' and consumers' evaluation of OHI quality. A qualitative evaluation of the interface with 16 participants revealed that PageGraph rendered the information and presentation nudges as intended. It provided the participants with easier access to quality indicators, encouraged fresh angles to assess information credibility, provided an evaluation framework, and encouraged validation of initial judgments. We then conducted a quantitative evaluation of the interface involving 60 participants using a between-subject experimental design. The control group used a regular web browser and evaluated the credibility of 12 preselected webpages, whereas the experimental group evaluated the same webpages with the assistance of PageGraph. PageGraph did not significantly influence participants' evaluation results. The results may be attributed to the insufficiency of the saliency and structure of the nudges implemented and the webpage stimuli's lack of sensitivity to the intervention. Future directions for applying nudges to support OHI evaluation were discussed.
    Date
    22. 6.2023 18:18:34
  3. Jiang, X.; Liu, J.: Extracting the evolutionary backbone of scientific domains : the semantic main path network analysis approach based on citation context analysis (2023) 0.02 
    0.018351128 = product of:
  0.055053383 = sum of:
    0.055053383 = weight(_text_:wide in 948) [ClassicSimilarity], result of:
      0.055053383 = score(doc=948,freq=2.0), product of:
        0.22492146 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.050763648 = queryNorm
        0.24476713 = fieldWeight in 948, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=948)
  0.33333334 = coord(1/3)
    Abstract
    Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT-based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top-K main paths extracted from various time slices of semantic citation network. In addition, a three-way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics-agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas.

Authors

Themes