Search (6 results, page 1 of 1)

  • × author_ss:"Liu, X."
  1. Chen, M.; Liu, X.; Qin, J.: Semantic relation extraction from socially-generated tags : a methodology for metadata generation (2008) 0.01 
    0.012488572 = product of:
  0.024977144 = sum of:
    0.013357237 = product of:
      0.053428948 = sum of:
        0.053428948 = weight(_text_:learning in 2648) [ClassicSimilarity], result of:
          0.053428948 = score(doc=2648,freq=4.0), product of:
            0.15317118 = queryWeight, product of:
              4.464877 = idf(docFreq=1382, maxDocs=44218)
              0.0343058 = queryNorm
            0.34881854 = fieldWeight in 2648, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.464877 = idf(docFreq=1382, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2648)
      0.25 = coord(1/4)
    0.011619906 = product of:
      0.023239812 = sum of:
        0.023239812 = weight(_text_:22 in 2648) [ClassicSimilarity], result of:
          0.023239812 = score(doc=2648,freq=2.0), product of:
            0.120133065 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0343058 = queryNorm
            0.19345059 = fieldWeight in 2648, 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=2648)
      0.5 = coord(1/2)
  0.5 = coord(2/4)
    Abstract
    The growing predominance of social semantics in the form of tagging presents the metadata community with both opportunities and challenges as for leveraging this new form of information content representation and for retrieval. One key challenge is the absence of contextual information associated with these tags. This paper presents an experiment working with Flickr tags as an example of utilizing social semantics sources for enriching subject metadata. The procedure included four steps: 1) Collecting a sample of Flickr tags, 2) Calculating cooccurrences between tags through mutual information, 3) Tracing contextual information of tag pairs via Google search results, 4) Applying natural language processing and machine learning techniques to extract semantic relations between tags. The experiment helped us to build a context sentence collection from the Google search results, which was then processed by natural language processing and machine learning algorithms. This new approach achieved a reasonably good rate of accuracy in assigning semantic relations to tag pairs. This paper also explores the implications of this approach for using social semantics to enrich subject metadata.
    Source
    Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
  2. Liu, X.; Guo, C.; Zhang, L.: Scholar metadata and knowledge generation with human and artificial intelligence (2014) 0.01 
    0.0056669954 = product of:
  0.022667982 = sum of:
    0.022667982 = product of:
      0.09067193 = sum of:
        0.09067193 = weight(_text_:learning in 1287) [ClassicSimilarity], result of:
          0.09067193 = score(doc=1287,freq=8.0), product of:
            0.15317118 = queryWeight, product of:
              4.464877 = idf(docFreq=1382, maxDocs=44218)
              0.0343058 = queryNorm
            0.59196466 = fieldWeight in 1287, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              4.464877 = idf(docFreq=1382, maxDocs=44218)
              0.046875 = fieldNorm(doc=1287)
      0.25 = coord(1/4)
  0.25 = coord(1/4)
    Abstract
    Scholar metadata have traditionally centered on descriptive representations, which have been used as a foundation for scholarly publication repositories and academic information retrieval systems. In this article, we propose innovative and economic methods of generating knowledge-based structural metadata (structural keywords) using a combination of natural language processing-based machine-learning techniques and human intelligence. By allowing low-barrier participation through a social media system, scholars (both as authors and users) can participate in the metadata editing and enhancing process and benefit from more accurate and effective information retrieval. Our experimental web system ScholarWiki uses machine learning techniques, which automatically produce increasingly refined metadata by learning from the structural metadata contributed by scholars. The cumulated structural metadata add intelligence and automatically enhance and update recursively the quality of metadata, wiki pages, and the machine-learning model.
  3. Liu, X.; Jia, H.: Answering academic questions for education by recommending cyberlearning resources (2013) 0.00 
    0.004907762 = product of:
  0.019631049 = sum of:
    0.019631049 = product of:
      0.078524195 = sum of:
        0.078524195 = weight(_text_:learning in 1012) [ClassicSimilarity], result of:
          0.078524195 = score(doc=1012,freq=6.0), product of:
            0.15317118 = queryWeight, product of:
              4.464877 = idf(docFreq=1382, maxDocs=44218)
              0.0343058 = queryNorm
            0.51265645 = fieldWeight in 1012, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.464877 = idf(docFreq=1382, maxDocs=44218)
              0.046875 = fieldNorm(doc=1012)
      0.25 = coord(1/4)
  0.25 = coord(1/4)
    Abstract
    In this study, we design an innovative method for answering students' or scholars' academic questions (for a specific scientific publication) by automatically recommending e-learning resources in a cyber-infrastructure-enabled learning environment to enhance the learning experiences of students and scholars. By using information retrieval and metasearch methodologies, different types of referential metadata (related Wikipedia pages, data sets, source code, video lectures, presentation slides, and online tutorials) for an assortment of publications and scientific topics will be automatically retrieved, associated, and ranked (via the language model and the inference network model) to provide easily understandable cyberlearning resources to answer students' questions. We also designed an experimental system to automatically answer students' questions for a specific academic publication and then evaluated the quality of the answers (the recommended resources) using mean reciprocal rank and normalized discounted cumulative gain. After examining preliminary evaluation results and student feedback, we found that cyberlearning resources can provide high-quality and straightforward answers for students' and scholars' questions concerning the content of academic publications.
  4. Liu, X.: Generating metadata for cyberlearning resources through information retrieval and meta-search (2013) 0.00 
    0.004007171 = product of:
  0.016028684 = sum of:
    0.016028684 = product of:
      0.064114735 = sum of:
        0.064114735 = weight(_text_:learning in 676) [ClassicSimilarity], result of:
          0.064114735 = score(doc=676,freq=4.0), product of:
            0.15317118 = queryWeight, product of:
              4.464877 = idf(docFreq=1382, maxDocs=44218)
              0.0343058 = queryNorm
            0.41858223 = fieldWeight in 676, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.464877 = idf(docFreq=1382, maxDocs=44218)
              0.046875 = fieldNorm(doc=676)
      0.25 = coord(1/4)
  0.25 = coord(1/4)
    Abstract
    The goal of this study was to propose novel cyberlearning resource-based scientific referential metadata for an assortment of publications and scientific topics, in order to enhance the learning experiences of students and scholars in a cyberinfrastructure-enabled learning environment. By using information retrieval and meta-search approaches, different types of referential metadata, such as related Wikipedia pages, data sets, source code, video lectures, presentation slides, and (online) tutorials for scientific publications and scientific topics will be automatically retrieved, associated, and ranked. In order to test our method of automatic cyberlearning referential metadata generation, we designed a user experiment to validate the quality of the metadata for each scientific keyword and publication and resource-ranking algorithm. Evaluation results show that the cyberlearning referential metadata retrieved via meta-search and statistical relevance ranking can help students better understand the essence of scientific keywords and publications.
  5. Liu, X.; Qin, J.: ¬An interactive metadata model for structural, descriptive, and referential representation of scholarly output (2014) 0.00 
    0.0033393092 = product of:
  0.013357237 = sum of:
    0.013357237 = product of:
      0.053428948 = sum of:
        0.053428948 = weight(_text_:learning in 1253) [ClassicSimilarity], result of:
          0.053428948 = score(doc=1253,freq=4.0), product of:
            0.15317118 = queryWeight, product of:
              4.464877 = idf(docFreq=1382, maxDocs=44218)
              0.0343058 = queryNorm
            0.34881854 = fieldWeight in 1253, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.464877 = idf(docFreq=1382, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1253)
      0.25 = coord(1/4)
  0.25 = coord(1/4)
    Abstract
    The scientific metadata model proposed in this article encompasses both classical descriptive metadata such as those defined in the Dublin Core Metadata Element Set (DC) and the innovative structural and referential metadata properties that go beyond the classical model. Structural metadata capture the structural vocabulary in research publications; referential metadata include not only citations but also data about other types of scholarly output that is based on or related to the same publication. The article describes the structural, descriptive, and referential (SDR) elements of the metadata model and explains the underlying assumptions and justifications for each major component in the model. ScholarWiki, an experimental system developed as a proof of concept, was built over the wiki platform to allow user interaction with the metadata and the editing, deleting, and adding of metadata. By allowing and encouraging scholars (both as authors and as users) to participate in the knowledge and metadata editing and enhancing process, the larger community will benefit from more accurate and effective information retrieval. The ScholarWiki system utilizes machine-learning techniques that can automatically produce self-enhanced metadata by learning from the structural metadata that scholars contribute, which will add intelligence to enhance and update automatically the publication of metadata Wiki pages.
  6. Chen, Z.; Huang, Y.; Tian, J.; Liu, X.; Fu, K.; Huang, T.: Joint model for subsentence-level sentiment analysis with Markov logic (2015) 0.00 
    0.002361248 = product of:
  0.009444992 = sum of:
    0.009444992 = product of:
      0.03777997 = sum of:
        0.03777997 = weight(_text_:learning in 2210) [ClassicSimilarity], result of:
          0.03777997 = score(doc=2210,freq=2.0), product of:
            0.15317118 = queryWeight, product of:
              4.464877 = idf(docFreq=1382, maxDocs=44218)
              0.0343058 = queryNorm
            0.24665193 = fieldWeight in 2210, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.464877 = idf(docFreq=1382, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2210)
      0.25 = coord(1/4)
  0.25 = coord(1/4)
    Abstract
    Sentiment analysis mainly focuses on the study of one's opinions that express positive or negative sentiments. With the explosive growth of web documents, sentiment analysis is becoming a hot topic in both academic research and system design. Fine-grained sentiment analysis is traditionally solved as a 2-step strategy, which results in cascade errors. Although joint models, such as joint sentiment/topic and maximum entropy (MaxEnt)/latent Dirichlet allocation, are proposed to tackle this problem of sentiment analysis, they focus on the joint learning of both aspects and sentiments. Thus, they are not appropriate to solve the cascade errors for sentiment analysis at the sentence or subsentence level. In this article, we present a novel jointly fine-grained sentiment analysis framework at the subsentence level with Markov logic. First, we divide the task into 2 separate stages (subjectivity classification and polarity classification). Then, the 2 separate stages are processed, respectively, with different feature sets, which are implemented by local formulas in Markov logic. Finally, global formulas in Markov logic are adopted to realize the interactions of the 2 separate stages. The joint inference of subjectivity and polarity helps prevent cascade errors. Experiments on a Chinese sentiment data set manifest that our joint model brings significant improvements.

Authors

Years

Themes