Search (7 results, page 1 of 1)

0.008418675 = product of:
  0.0336747 = sum of:
    0.0336747 = product of:
      0.0673494 = sum of:
        0.0673494 = weight(_text_:model in 4366) [ClassicSimilarity], result of:
          0.0673494 = score(doc=4366,freq=6.0), product of:
            0.1830527 = queryWeight, product of:
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.047605187 = queryNorm
            0.36792353 = fieldWeight in 4366, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4366)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

An innovative model to measure the influence among scientific journals is developed in this study. This model is based on the path analysis of a journal citation network, and its output is a journal influence matrix that describes the directed influence among all journals. Based on this model, an index of journals' overall influence, the quality-structure index (QSI), is derived. Journal ranking based on QSI has the advantage of accounting for both intrinsic journal quality and the structural position of a journal in a citation network. The QSI also integrates the characteristics of two prevailing streams of journal-assessment measures: those based on bibliometric statistics to approximate intrinsic journal quality, such as the Journal Impact Factor, and those using a journal's structural position based on the PageRank-type of algorithm, such as the Eigenfactor score. Empirical results support our finding that the new index is significantly closer to scholars' subjective perception of journal influence than are the two aforementioned measures. In addition, the journal influence matrix offers a new way to measure two-way influences between any two academic journals, hence establishing a theoretical basis for future scientometrics studies to investigate the knowledge flow within and across research disciplines.

0.006873818 = product of:
  0.027495272 = sum of:
    0.027495272 = product of:
      0.054990545 = sum of:
        0.054990545 = weight(_text_:model in 5263) [ClassicSimilarity], result of:
          0.054990545 = score(doc=5263,freq=4.0), product of:
            0.1830527 = queryWeight, product of:
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.047605187 = queryNorm
            0.30040827 = fieldWeight in 5263, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5263)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

Recent years have seen the rise of user-generated con-tents (UGCs) in online social media. Diverse UGC sources and information overload are making it increasingly difficult to satisfy personalized information needs. To organize UGCs in a user-centered way, we should not only map them based on textual top-ics but also link them with users and even user communities. We propose a multi-dimensional framework to organize information by connecting UGCs, users, and user communities. First, we use a topic model to generate a topic hierarchy from UGCs. Second, an author-topic model is applied to learn user interests. Third, user communities are detected through a label propagation algo-rithm. Finally, a multi-dimensional information organization pat-tern is formulated based on similarities among the topic hierar-chies of UGCs, user interests, and user communities. The results reveal that: 1) our proposed framework can organize information rom multiple sources in a user-centered way; 2) hierarchical topic structures can provide comprehensive and in-depth topics for us-ers; and, 3) user communities are efficient in helping people to connect with others who have similar interests.

0.006873818 = product of:
  0.027495272 = sum of:
    0.027495272 = product of:
      0.054990545 = sum of:
        0.054990545 = weight(_text_:model in 5816) [ClassicSimilarity], result of:
          0.054990545 = score(doc=5816,freq=4.0), product of:
            0.1830527 = queryWeight, product of:
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.047605187 = queryNorm
            0.30040827 = fieldWeight in 5816, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5816)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

Millions of messages are produced on microblog platforms every day, leading to the pressing need for automatic identification of key points from the massive texts. To absorb salient content from the vast bulk of microblog posts, this article focuses on the task of microblog keyphrase extraction. In previous work, most efforts treat messages as independent documents and might suffer from the data sparsity problem exhibited in short and informal microblog posts. On the contrary, we propose to enrich contexts via exploiting conversations initialized by target posts and formed by their replies, which are generally centered around relevant topics to the target posts and therefore helpful for keyphrase identification. Concretely, we present a neural keyphrase extraction framework, which has 2 modules: a conversation context encoder and a keyphrase tagger. The conversation context encoder captures indicative representation from their conversation contexts and feeds the representation into the keyphrase tagger, and the keyphrase tagger extracts salient words from target posts. The 2 modules were trained jointly to optimize the conversation context encoding and keyphrase extraction processes. In the conversation context encoder, we leverage hierarchical structures to capture the word-level indicative representation and message-level indicative representation hierarchically. In both of the modules, we apply character-level representations, which enables the model to explore morphological features and deal with the out-of-vocabulary problem caused by the informal language style of microblog messages. Extensive comparison results on real-life data sets indicate that our model outperforms state-of-the-art models from previous studies.

0.006873818 = product of:
  0.027495272 = sum of:
    0.027495272 = product of:
      0.054990545 = sum of:
        0.054990545 = weight(_text_:model in 237) [ClassicSimilarity], result of:
          0.054990545 = score(doc=237,freq=4.0), product of:
            0.1830527 = queryWeight, product of:
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.047605187 = queryNorm
            0.30040827 = fieldWeight in 237, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.0390625 = fieldNorm(doc=237)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

The premise of manual keyphrase annotation is to read the corresponding content of an annotated object. Intuitively, when we read, more important words will occupy a longer reading time. Hence, by leveraging human reading time, we can find the salient words in the corresponding content. However, previous studies on keyphrase extraction ignore human reading features. In this article, we aim to leverage human reading time to extract keyphrases from microblog posts. There are two main tasks in this study. One is to determine how to measure the time spent by a human on reading a word. We use eye fixation durations (FDs) extracted from an open source eye-tracking corpus. Moreover, we propose strategies to make eye FD more effective on keyphrase extraction. The other task is to determine how to integrate human reading time into keyphrase extraction models. We propose two novel neural network models. The first is a model in which the human reading time is used as the ground truth of the attention mechanism. In the second model, we use human reading time as the external feature. Quantitative and qualitative experiments show that our proposed models yield better performance than the baseline models on two microblog datasets.

0.0058326283 = product of:
  0.023330513 = sum of:
    0.023330513 = product of:
      0.046661027 = sum of:
        0.046661027 = weight(_text_:model in 1301) [ClassicSimilarity], result of:
          0.046661027 = score(doc=1301,freq=2.0), product of:
            0.1830527 = queryWeight, product of:
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.047605187 = queryNorm
            0.25490487 = fieldWeight in 1301, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.046875 = fieldNorm(doc=1301)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

In the age of mobile commerce, users receive floods of commercial messages. How do users judge the relevance of such information? Is their relevance judgment affected by contextual factors, such as location and time? How do message content and contextual factors affect users' privacy concerns? With a focus on mobile ads, we propose a research model based on theories of relevance judgment and mobile marketing research. We suggest topicality, reliability, and economic value as key content factors and location and time as key contextual factors. We found mobile relevance judgment is affected mainly by content factors, whereas privacy concerns are affected by both content and contextual factors. Moreover, topicality and economic value have a synergetic effect that makes a message more relevant. Higher topicality and location precision exacerbate privacy concerns, whereas message reliability alleviates privacy concerns caused by location precision. These findings reveal an interesting intricacy in user relevance judgment and privacy concerns and provide nuanced guidance for the design and delivery of mobile commercial information.

0.0058326283 = product of:
  0.023330513 = sum of:
    0.023330513 = product of:
      0.046661027 = sum of:
        0.046661027 = weight(_text_:model in 5219) [ClassicSimilarity], result of:
          0.046661027 = score(doc=5219,freq=2.0), product of:
            0.1830527 = queryWeight, product of:
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.047605187 = queryNorm
            0.25490487 = fieldWeight in 5219, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.046875 = fieldNorm(doc=5219)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

0.0040311534 = product of:
  0.016124614 = sum of:
    0.016124614 = product of:
      0.032249227 = sum of:
        0.032249227 = weight(_text_:22 in 4637) [ClassicSimilarity], result of:
          0.032249227 = score(doc=4637,freq=2.0), product of:
            0.16670525 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.047605187 = queryNorm
            0.19345059 = fieldWeight in 4637, 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=4637)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

20. 1.2015 18:30:22