Search (8 results, page 1 of 1)

0.034150656 = product of:
  0.15936972 = sum of:
    0.02546139 = weight(_text_:subject in 5588) [ClassicSimilarity], result of:
      0.02546139 = score(doc=5588,freq=2.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.23709705 = fieldWeight in 5588, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.046875 = fieldNorm(doc=5588)
    0.066954166 = weight(_text_:classification in 5588) [ClassicSimilarity], result of:
      0.066954166 = score(doc=5588,freq=22.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.70019996 = fieldWeight in 5588, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=5588)
    0.066954166 = weight(_text_:classification in 5588) [ClassicSimilarity], result of:
      0.066954166 = score(doc=5588,freq=22.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.70019996 = fieldWeight in 5588, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=5588)
  0.21428572 = coord(3/14)

Abstract

The standardization of Chinese materials classification was first proposed in the late-1970s in China. In December 1980, the CCDST, the Chinese Library Association and the Chinese Society for Information Science proposed that the Chinese Library Classification system be adopted as national standard. This marked the beginning of the standardization of Chinese materials classification. Later on, there were many conferences and workshops held and four draft national standards were discussed, those for the Chinese Library Classification systems, the Materials Classification System, the Rules for Thesaurus and Subject Headings, and the rules for Materials Classifying Color Recognition. This article gives a brief review on the historical development of the standardization on Chinese Library Classification. It also discusses its effects on automation, networking and resources sharing and the feasibility of adopting Chinese Library Classification as a National Standard. In addition, the main content of the standardization of materials classification, use of the national standard classification system and variations under the standard system are covered in this article

Source

Cataloging and classification quarterly. 16(1993) no.2, S.41-54

0.01153568 = product of:
  0.08074976 = sum of:
    0.04037488 = weight(_text_:classification in 122) [ClassicSimilarity], result of:
      0.04037488 = score(doc=122,freq=8.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.42223644 = fieldWeight in 122, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=122)
    0.04037488 = weight(_text_:classification in 122) [ClassicSimilarity], result of:
      0.04037488 = score(doc=122,freq=8.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.42223644 = fieldWeight in 122, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=122)
  0.14285715 = coord(2/14)

Abstract

This paper reports on a portion of a larger ongoing project. We address the issues of information organization and retrieval in large, active commercial websites. More specifically, we address the use of classification for providing access to the contents of such sites. We approach this analysis by describing the functionality and structure of the classification scheme of one such representative, large, active, commercial websites: eBay.com, a web-based auction site for millions of users and items. We compare eBay's classification scheme with the Art & Architecture Thesaurus, which is a tool for describing and providing access to material culture.

0.008156957 = product of:
  0.057098698 = sum of:
    0.028549349 = weight(_text_:classification in 74) [ClassicSimilarity], result of:
      0.028549349 = score(doc=74,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.29856625 = fieldWeight in 74, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=74)
    0.028549349 = weight(_text_:classification in 74) [ClassicSimilarity], result of:
      0.028549349 = score(doc=74,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.29856625 = fieldWeight in 74, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=74)
  0.14285715 = coord(2/14)

Abstract

Digital libraries have become one of the most important Web services for information seeking. One of their main drawbacks is their global approach: In general, there is just one interface for all users. One of the key elements in improving user satisfaction in digital libraries is personalization. When considering personalizing factors, cognitive styles have been proved to be one of the relevant parameters that affect information seeking. This justifies the introduction of cognitive style as one of the parameters of a Web personalized service. Nevertheless, this approach has one major drawback: Each user has to run a time-consuming test that determines his or her cognitive style. In this article, we present a study of how different classification systems can be used to automatically identify the cognitive style of a user using the set of interactions with a digital library. These classification systems can be used to automatically personalize, from a cognitive-style point of view, the interaction of the digital library and each of its users.

0.0067974646 = product of:
  0.04758225 = sum of:
    0.023791125 = weight(_text_:classification in 2210) [ClassicSimilarity], result of:
      0.023791125 = score(doc=2210,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.24880521 = fieldWeight in 2210, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2210)
    0.023791125 = weight(_text_:classification in 2210) [ClassicSimilarity], result of:
      0.023791125 = score(doc=2210,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.24880521 = fieldWeight in 2210, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2210)
  0.14285715 = coord(2/14)

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.

0.005739506 = product of:
  0.04017654 = sum of:
    0.030006537 = weight(_text_:subject in 2648) [ClassicSimilarity], result of:
      0.030006537 = score(doc=2648,freq=4.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.27942157 = fieldWeight in 2648, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2648)
    0.010170003 = product of:
      0.020340007 = sum of:
        0.020340007 = weight(_text_:22 in 2648) [ClassicSimilarity], result of:
          0.020340007 = score(doc=2648,freq=2.0), product of:
            0.10514317 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03002521 = 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)
  0.14285715 = coord(2/14)

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

0.004806533 = product of:
  0.03364573 = sum of:
    0.016822865 = weight(_text_:classification in 847) [ClassicSimilarity], result of:
      0.016822865 = score(doc=847,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.17593184 = fieldWeight in 847, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=847)
    0.016822865 = weight(_text_:classification in 847) [ClassicSimilarity], result of:
      0.016822865 = score(doc=847,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.17593184 = fieldWeight in 847, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=847)
  0.14285715 = coord(2/14)

Abstract

This study investigates scholars' citation behaviors from a fine-grained perspective. Specifically, each scholarly citation is considered multidimensional rather than logically unidimensional (i.e., present or absent). Thirty million articles from PubMed were accessed for use in empirical research, in which a total of 15 interpretable features of scholarly citations were constructed and grouped into three main categories. Each category corresponds to one aspect of the reasons and motivations behind scholars' citation decision-making during academic writing. Using about 500,000 pairs of actual and randomly generated scholarly citations, a series of Random Forest-based classification experiments were conducted to quantitatively evaluate the correlation between each constructed citation feature and citation decisions made by scholars. Our experimental results indicate that citation proximity is the category most relevant to scholars' citation decision-making, followed by citation authority and citation inertia. However, big-name scholars whose h-indexes rank among the top 1% exhibit a unique pattern of citation behaviors-their citation decision-making correlates most closely with citation inertia, with the correlation nearly three times as strong as that of their ordinary counterparts. Hopefully, the empirical findings presented in this paper can bring us closer to characterizing and understanding the complex process of generating scholarly citations in academia.

0.0018186709 = product of:
  0.02546139 = sum of:
    0.02546139 = weight(_text_:subject in 1035) [ClassicSimilarity], result of:
      0.02546139 = score(doc=1035,freq=2.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.23709705 = fieldWeight in 1035, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.046875 = fieldNorm(doc=1035)
  0.071428575 = coord(1/14)

Abstract

User interest as a very dynamic information need is often ignored in most existing information retrieval systems. In this research, we present the results of experiments designed to evaluate the performance of a real-time interest model (RIM) that attempts to identify the dynamic and changing query level interests regarding social media outputs. Unlike most existing ranking methods, our ranking approach targets calculation of the probability that user interest in the content of the document is subject to very dynamic user interest change. We describe 2 formulations of the model (real-time interest vector space and real-time interest language model) stemming from classical relevance ranking methods and develop a novel methodology for evaluating the performance of RIM using Amazon Mechanical Turk to collect (interest-based) relevance judgments on a daily basis. Our results show that the model usually, although not always, performs better than baseline results obtained from commercial web search engines. We identify factors that affect RIM performance and outline plans for future research.

0.001780432 = product of:
  0.024926046 = sum of:
    0.024926046 = product of:
      0.04985209 = sum of:
        0.04985209 = weight(_text_:texts in 4277) [ClassicSimilarity], result of:
          0.04985209 = score(doc=4277,freq=2.0), product of:
            0.16460659 = queryWeight, product of:
              5.4822793 = idf(docFreq=499, maxDocs=44218)
              0.03002521 = queryNorm
            0.302856 = fieldWeight in 4277, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.4822793 = idf(docFreq=499, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4277)
      0.5 = coord(1/2)
  0.071428575 = coord(1/14)

Abstract

This chapter reviews research and applications in statistical language modeling for information retrieval (IR), which has emerged within the past several years as a new probabilistic framework for describing information retrieval processes. Generally speaking, statistical language modeling, or more simply language modeling (LM), involves estimating a probability distribution that captures statistical regularities of natural language use. Applied to information retrieval, language modeling refers to the problem of estimating the likelihood that a query and a document could have been generated by the same language model, given the language model of the document either with or without a language model of the query. The roots of statistical language modeling date to the beginning of the twentieth century when Markov tried to model letter sequences in works of Russian literature (Manning & Schütze, 1999). Zipf (1929, 1932, 1949, 1965) studied the statistical properties of text and discovered that the frequency of works decays as a Power function of each works rank. However, it was Shannon's (1951) work that inspired later research in this area. In 1951, eager to explore the applications of his newly founded information theory to human language, Shannon used a prediction game involving n-grams to investigate the information content of English text. He evaluated n-gram models' performance by comparing their crossentropy an texts with the true entropy estimated using predictions made by human subjects. For many years, statistical language models have been used primarily for automatic speech recognition. Since 1980, when the first significant language model was proposed (Rosenfeld, 2000), statistical language modeling has become a fundamental component of speech recognition, machine translation, and spelling correction.