Search (61 results, page 1 of 4)

  • × author_ss:"Chen, H."
  1. Dumais, S.; Chen, H.: Hierarchical classification of Web content (2000) 0.04 
    0.038817003 = product of:
  0.064695 = sum of:
    0.0078809485 = weight(_text_:s in 492) [ClassicSimilarity], result of:
      0.0078809485 = score(doc=492,freq=4.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.20385705 = fieldWeight in 492, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.09375 = fieldNorm(doc=492)
    0.050546348 = weight(_text_:u in 492) [ClassicSimilarity], result of:
      0.050546348 = score(doc=492,freq=2.0), product of:
        0.116430275 = queryWeight, product of:
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.035557263 = queryNorm
        0.43413407 = fieldWeight in 492, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.09375 = fieldNorm(doc=492)
    0.0062677055 = weight(_text_:a in 492) [ClassicSimilarity], result of:
      0.0062677055 = score(doc=492,freq=2.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.15287387 = fieldWeight in 492, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.09375 = fieldNorm(doc=492)
  0.6 = coord(3/5)
    Pages
    S.256-263
    Source
    Proceedings of ACM SIGIR 23rd International Conference on Research and Development in Information Retrieval. Ed. by N.J. Belkin, P. Ingwersen u. M.K. Leong
    Type
    a
  2. Schatz, B.R.; Johnson, E.H.; Cochrane, P.A.; Chen, H.: Interactive term suggestion for users of digital libraries : using thesauri and co-occurrence lists for information retrieval (1996) 0.03 
    0.031193366 = product of:
  0.05198894 = sum of:
    0.004643894 = weight(_text_:s in 6417) [ClassicSimilarity], result of:
      0.004643894 = score(doc=6417,freq=2.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.120123915 = fieldWeight in 6417, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.078125 = fieldNorm(doc=6417)
    0.042121958 = weight(_text_:u in 6417) [ClassicSimilarity], result of:
      0.042121958 = score(doc=6417,freq=2.0), product of:
        0.116430275 = queryWeight, product of:
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.035557263 = queryNorm
        0.3617784 = fieldWeight in 6417, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.078125 = fieldNorm(doc=6417)
    0.0052230875 = weight(_text_:a in 6417) [ClassicSimilarity], result of:
      0.0052230875 = score(doc=6417,freq=2.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.12739488 = fieldWeight in 6417, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.078125 = fieldNorm(doc=6417)
  0.6 = coord(3/5)
    Pages
    S.126-133
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
    Type
    a
  3. Chen, H.; Martinez, J.; Kirchhoff, A.; Ng, T.D.; Schatz, B.R.: Alleviating search uncertainty through concept associations : automatic indexing, co-occurence analysis, and parallel computing (1998) 0.02 
    0.02144151 = product of:
  0.03573585 = sum of:
    0.002786336 = weight(_text_:s in 5202) [ClassicSimilarity], result of:
      0.002786336 = score(doc=5202,freq=2.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.072074346 = fieldWeight in 5202, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.046875 = fieldNorm(doc=5202)
    0.025273174 = weight(_text_:u in 5202) [ClassicSimilarity], result of:
      0.025273174 = score(doc=5202,freq=2.0), product of:
        0.116430275 = queryWeight, product of:
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.035557263 = queryNorm
        0.21706703 = fieldWeight in 5202, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.046875 = fieldNorm(doc=5202)
    0.0076763397 = weight(_text_:a in 5202) [ClassicSimilarity], result of:
      0.0076763397 = score(doc=5202,freq=12.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.18723148 = fieldWeight in 5202, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=5202)
  0.6 = coord(3/5)
    Abstract
    In this article, we report research on an algorithmic approach to alleviating search uncertainty in a large information space. Grounded on object filtering, automatic indexing, and co-occurence analysis, we performed a large-scale experiment using a parallel supercomputer (SGI Power Challenge) to analyze 400.000+ abstracts in an INSPEC computer engineering collection. Two system-generated thesauri, one based on a combined object filtering and automatic indexing method, and the other based on automatic indexing only, were compaed with the human-generated INSPEC subject thesaurus. Our user evaluation revealed that the system-generated thesauri were better than the INSPEC thesaurus in 'concept recall', but in 'concept precision' the 3 thesauri were comparable. Our analysis also revealed that the terms suggested by the 3 thesauri were complementary and could be used to significantly increase 'variety' in search terms the thereby reduce search uncertainty
    Source
    Journal of the American Society for Information Science. 49(1998) no.3, S.206-216
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
    Type
    a
  4. Chen, H.; Zhang, Y.; Houston, A.L.: Semantic indexing and searching using a Hopfield net (1998) 0.02 
    0.021040212 = product of:
  0.035067018 = sum of:
    0.002786336 = weight(_text_:s in 5704) [ClassicSimilarity], result of:
      0.002786336 = score(doc=5704,freq=2.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.072074346 = fieldWeight in 5704, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.046875 = fieldNorm(doc=5704)
    0.025273174 = weight(_text_:u in 5704) [ClassicSimilarity], result of:
      0.025273174 = score(doc=5704,freq=2.0), product of:
        0.116430275 = queryWeight, product of:
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.035557263 = queryNorm
        0.21706703 = fieldWeight in 5704, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.046875 = fieldNorm(doc=5704)
    0.007007508 = weight(_text_:a in 5704) [ClassicSimilarity], result of:
      0.007007508 = score(doc=5704,freq=10.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.1709182 = fieldWeight in 5704, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=5704)
  0.6 = coord(3/5)
    Abstract
    Presents a neural network approach to document semantic indexing. Reports results of a study to apply a Hopfield net algorithm to simulate human associative memory for concept exploration in the domain of computer science and engineering. The INSPEC database, consisting of 320.000 abstracts from leading periodical articles was used as the document test bed. Benchmark tests conformed that 3 parameters: maximum number of activated nodes; maximum allowable error; and maximum number of iterations; were useful in positively influencing network convergence behaviour without negatively impacting central processing unit performance. Another series of benchmark tests was performed to determine the effectiveness of various filtering techniques in reducing the negative impact of noisy input terms. Preliminary user tests conformed expectations that the Hopfield net is potentially useful as an associative memory technique to improve document recall and precision by solving discrepancies between indexer vocabularies and end user vocabularies
    Source
    Journal of information science. 24(1998) no.1, S.3-18
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
    Type
    a
  5. Chen, H.; Lally, A.M.; Zhu, B.; Chau, M.: HelpfulMed : Intelligent searching for medical information over the Internet (2003) 0.02 
    0.017867927 = product of:
  0.029779878 = sum of:
    0.002321947 = weight(_text_:s in 1615) [ClassicSimilarity], result of:
      0.002321947 = score(doc=1615,freq=2.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.060061958 = fieldWeight in 1615, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1615)
    0.021060979 = weight(_text_:u in 1615) [ClassicSimilarity], result of:
      0.021060979 = score(doc=1615,freq=2.0), product of:
        0.116430275 = queryWeight, product of:
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.035557263 = queryNorm
        0.1808892 = fieldWeight in 1615, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1615)
    0.00639695 = weight(_text_:a in 1615) [ClassicSimilarity], result of:
      0.00639695 = score(doc=1615,freq=12.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.15602624 = fieldWeight in 1615, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1615)
  0.6 = coord(3/5)
    Abstract
    The Medical professionals and researchers need information from reputable sources to accomplish their work. Unfortunately, the Web has a large number of documents that are irrelevant to their work, even those documents that purport to be "medically-related." This paper describes an architecture designed to integrate advanced searching and indexing algorithms, an automatic thesaurus, or "concept space," and Kohonen-based Self-Organizing Map (SOM) technologies to provide searchers with finegrained results. Initial results indicate that these systems provide complementary retrieval functionalities. HelpfulMed not only allows users to search Web pages and other online databases, but also allows them to build searches through the use of an automatic thesaurus and browse a graphical display of medical-related topics. Evaluation results for each of the different components are included. Our spidering algorithm outperformed both breadth-first search and PageRank spiders an a test collection of 100,000 Web pages. The automatically generated thesaurus performed as well as both MeSH and UMLS-systems which require human mediation for currency. Lastly, a variant of the Kohonen SOM was comparable to MeSH terms in perceived cluster precision and significantly better at perceived cluster recall.
    Footnote
    Teil eines Themenheftes: "Web retrieval and mining: A machine learning perspective"
    Source
    Journal of the American Society for Information Science and technology. 54(2003) no.7, S.683-694
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
    Type
    a
  6. Chung, W.; Chen, H.: Browsing the underdeveloped Web : an experiment on the Arabic Medical Web Directory (2009) 0.02 
    0.015318172 = product of:
  0.025530286 = sum of:
    0.002786336 = weight(_text_:s in 2733) [ClassicSimilarity], result of:
      0.002786336 = score(doc=2733,freq=2.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.072074346 = fieldWeight in 2733, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.046875 = fieldNorm(doc=2733)
    0.008291395 = weight(_text_:a in 2733) [ClassicSimilarity], result of:
      0.008291395 = score(doc=2733,freq=14.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.20223314 = fieldWeight in 2733, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=2733)
    0.014452554 = product of:
      0.028905109 = sum of:
        0.028905109 = weight(_text_:22 in 2733) [ClassicSimilarity], result of:
          0.028905109 = score(doc=2733,freq=2.0), product of:
            0.124515474 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.035557263 = queryNorm
            0.23214069 = fieldWeight in 2733, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=2733)
      0.5 = coord(1/2)
  0.6 = coord(3/5)
    Abstract
    While the Web has grown significantly in recent years, some portions of the Web remain largely underdeveloped, as shown in a lack of high-quality content and functionality. An example is the Arabic Web, in which a lack of well-structured Web directories limits users' ability to browse for Arabic resources. In this research, we proposed an approach to building Web directories for the underdeveloped Web and developed a proof-of-concept prototype called the Arabic Medical Web Directory (AMedDir) that supports browsing of over 5,000 Arabic medical Web sites and pages organized in a hierarchical structure. We conducted an experiment involving Arab participants and found that the AMedDir significantly outperformed two benchmark Arabic Web directories in terms of browsing effectiveness, efficiency, information quality, and user satisfaction. Participants expressed strong preference for the AMedDir and provided many positive comments. This research thus contributes to developing a useful Web directory for organizing the information in the Arabic medical domain and to a better understanding of how to support browsing on the underdeveloped Web.
    Date
    22. 3.2009 17:57:50
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.3, S.595-607
    Type
    a
  7. Carmel, E.; Crawford, S.; Chen, H.: Browsing in hypertext : a cognitive study (1992) 0.01 
    0.013342212 = product of:
  0.02223702 = sum of:
    0.0032837286 = weight(_text_:s in 7469) [ClassicSimilarity], result of:
      0.0032837286 = score(doc=7469,freq=4.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.08494043 = fieldWeight in 7469, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0390625 = fieldNorm(doc=7469)
    0.0069094957 = weight(_text_:a in 7469) [ClassicSimilarity], result of:
      0.0069094957 = score(doc=7469,freq=14.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.1685276 = fieldWeight in 7469, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0390625 = fieldNorm(doc=7469)
    0.012043796 = product of:
      0.024087591 = sum of:
        0.024087591 = weight(_text_:22 in 7469) [ClassicSimilarity], result of:
          0.024087591 = score(doc=7469,freq=2.0), product of:
            0.124515474 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.035557263 = queryNorm
            0.19345059 = fieldWeight in 7469, 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=7469)
      0.5 = coord(1/2)
  0.6 = coord(3/5)
    Abstract
    With the growth of hypertext and multimedia applications that support and encourage browsing it is time to take a penetrating look at browsing behaviour. Several dimensions of browsing are exemined, to find out: first, what is browsing and what cognitive processes are associated with it: second, is there a browsing strategy, and if so, are there any differences between how subject-area experts and novices browse; and finally, how can this knowledge be applied to improve the design of hypertext systems. Two groups of students, subject-area experts and novices, were studied while browsing a Macintosh HyperCard application on the subject The Vietnam War. A protocol analysis technique was used to gather and analyze data. Components of the GOMS model were used to describe the goals, operators, methods, and selection rules observed: Three browsing strategies were identified: (1) search-oriented browse, scanning and and reviewing information relevant to a fixed task; (2) review-browse, scanning and reviewing intersting information in the presence of transient browse goals that represent changing tasks, and (3) scan-browse, scanning for interesting information (without review). Most subjects primarily used review-browse interspersed with search-oriented browse. Within this strategy, comparisons between subject-area experts and novices revealed differences in tactics: experts browsed in more depth, seldom used referential links, selected different kinds of topics, and viewed information differently thatn did novices. Based on these findings, suggestions are made to hypertext developers
    Source
    IEEE transactions on systems, man and cybernetics. 22(1992) no.5, S.865-884
    Type
    a
  8. Hu, D.; Kaza, S.; Chen, H.: Identifying significant facilitators of dark network evolution (2009) 0.01 
    0.012700269 = product of:
  0.021167114 = sum of:
    0.0032837286 = weight(_text_:s in 2753) [ClassicSimilarity], result of:
      0.0032837286 = score(doc=2753,freq=4.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.08494043 = fieldWeight in 2753, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2753)
    0.00583959 = weight(_text_:a in 2753) [ClassicSimilarity], result of:
      0.00583959 = score(doc=2753,freq=10.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.14243183 = fieldWeight in 2753, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2753)
    0.012043796 = product of:
      0.024087591 = sum of:
        0.024087591 = weight(_text_:22 in 2753) [ClassicSimilarity], result of:
          0.024087591 = score(doc=2753,freq=2.0), product of:
            0.124515474 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.035557263 = queryNorm
            0.19345059 = fieldWeight in 2753, 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=2753)
      0.5 = coord(1/2)
  0.6 = coord(3/5)
    Abstract
    Social networks evolve over time with the addition and removal of nodes and links to survive and thrive in their environments. Previous studies have shown that the link-formation process in such networks is influenced by a set of facilitators. However, there have been few empirical evaluations to determine the important facilitators. In a research partnership with law enforcement agencies, we used dynamic social-network analysis methods to examine several plausible facilitators of co-offending relationships in a large-scale narcotics network consisting of individuals and vehicles. Multivariate Cox regression and a two-proportion z-test on cyclic and focal closures of the network showed that mutual acquaintance and vehicle affiliations were significant facilitators for the network under study. We also found that homophily with respect to age, race, and gender were not good predictors of future link formation in these networks. Moreover, we examined the social causes and policy implications for the significance and insignificance of various facilitators including common jails on future co-offending. These findings provide important insights into the link-formation processes and the resilience of social networks. In addition, they can be used to aid in the prediction of future links. The methods described can also help in understanding the driving forces behind the formation and evolution of social networks facilitated by mobile and Web technologies.
    Date
    22. 3.2009 18:50:30
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.4, S.655-665
    Type
    a
  9. Zheng, R.; Li, J.; Chen, H.; Huang, Z.: ¬A framework for authorship identification of online messages : writing-style features and classification techniques (2006) 0.01 
    0.012457617 = product of:
  0.020762693 = sum of:
    0.002321947 = weight(_text_:s in 5276) [ClassicSimilarity], result of:
      0.002321947 = score(doc=5276,freq=2.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.060061958 = fieldWeight in 5276, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5276)
    0.00639695 = weight(_text_:a in 5276) [ClassicSimilarity], result of:
      0.00639695 = score(doc=5276,freq=12.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.15602624 = fieldWeight in 5276, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5276)
    0.012043796 = product of:
      0.024087591 = sum of:
        0.024087591 = weight(_text_:22 in 5276) [ClassicSimilarity], result of:
          0.024087591 = score(doc=5276,freq=2.0), product of:
            0.124515474 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.035557263 = queryNorm
            0.19345059 = fieldWeight in 5276, 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=5276)
      0.5 = coord(1/2)
  0.6 = coord(3/5)
    Abstract
    With the rapid proliferation of Internet technologies and applications, misuse of online messages for inappropriate or illegal purposes has become a major concern for society. The anonymous nature of online-message distribution makes identity tracing a critical problem. We developed a framework for authorship identification of online messages to address the identity-tracing problem. In this framework, four types of writing-style features (lexical, syntactic, structural, and content-specific features) are extracted and inductive learning algorithms are used to build feature-based classification models to identify authorship of online messages. To examine this framework, we conducted experiments on English and Chinese online-newsgroup messages. We compared the discriminating power of the four types of features and of three classification techniques: decision trees, backpropagation neural networks, and support vector machines. The experimental results showed that the proposed approach was able to identify authors of online messages with satisfactory accuracy of 70 to 95%. All four types of message features contributed to discriminating authors of online messages. Support vector machines outperformed the other two classification techniques in our experiments. The high performance we achieved for both the English and Chinese datasets showed the potential of this approach in a multiple-language context.
    Date
    22. 7.2006 16:14:37
    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.3, S.378-393
    Type
    a
  10. Leroy, G.; Chen, H.: Genescene: an ontology-enhanced integration of linguistic and co-occurrence based relations in biomedical texts (2005) 0.01 
    0.012123201 = product of:
  0.020205334 = sum of:
    0.002321947 = weight(_text_:s in 5259) [ClassicSimilarity], result of:
      0.002321947 = score(doc=5259,freq=2.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.060061958 = fieldWeight in 5259, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5259)
    0.00583959 = weight(_text_:a in 5259) [ClassicSimilarity], result of:
      0.00583959 = score(doc=5259,freq=10.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.14243183 = fieldWeight in 5259, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5259)
    0.012043796 = product of:
      0.024087591 = sum of:
        0.024087591 = weight(_text_:22 in 5259) [ClassicSimilarity], result of:
          0.024087591 = score(doc=5259,freq=2.0), product of:
            0.124515474 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.035557263 = queryNorm
            0.19345059 = fieldWeight in 5259, 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=5259)
      0.5 = coord(1/2)
  0.6 = coord(3/5)
    Abstract
    The increasing amount of publicly available literature and experimental data in biomedicine makes it hard for biomedical researchers to stay up-to-date. Genescene is a toolkit that will help alleviate this problem by providing an overview of published literature content. We combined a linguistic parser with Concept Space, a co-occurrence based semantic net. Both techniques extract complementary biomedical relations between noun phrases from MEDLINE abstracts. The parser extracts precise and semantically rich relations from individual abstracts. Concept Space extracts relations that hold true for the collection of abstracts. The Gene Ontology, the Human Genome Nomenclature, and the Unified Medical Language System, are also integrated in Genescene. Currently, they are used to facilitate the integration of the two relation types, and to select the more interesting and high-quality relations for presentation. A user study focusing on p53 literature is discussed. All MEDLINE abstracts discussing p53 were processed in Genescene. Two researchers evaluated the terms and relations from several abstracts of interest to them. The results show that the terms were precise (precision 93%) and relevant, as were the parser relations (precision 95%). The Concept Space relations were more precise when selected with ontological knowledge (precision 78%) than without (60%).
    Date
    22. 7.2006 14:26:01
    Source
    Journal of the American Society for Information Science and Technology. 56(2005) no.5, S.457-468
    Type
    a
  11. Chen, H.: Knowledge-based document retrieval : framework and design (1992) 0.01 
    0.0063148676 = product of:
  0.01578717 = sum of:
    0.00743023 = weight(_text_:s in 5283) [ClassicSimilarity], result of:
      0.00743023 = score(doc=5283,freq=2.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.19219826 = fieldWeight in 5283, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.125 = fieldNorm(doc=5283)
    0.00835694 = weight(_text_:a in 5283) [ClassicSimilarity], result of:
      0.00835694 = score(doc=5283,freq=2.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.20383182 = fieldWeight in 5283, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.125 = fieldNorm(doc=5283)
  0.4 = coord(2/5)
    Source
    Journal of information science. 18(1992), S.293-314
    Type
    a
  12. Chen, H.: Generating, integrating and activating thesauri for concept-based document retrieval (1993) 0.01 
    0.0063148676 = product of:
  0.01578717 = sum of:
    0.00743023 = weight(_text_:s in 7623) [ClassicSimilarity], result of:
      0.00743023 = score(doc=7623,freq=2.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.19219826 = fieldWeight in 7623, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.125 = fieldNorm(doc=7623)
    0.00835694 = weight(_text_:a in 7623) [ClassicSimilarity], result of:
      0.00835694 = score(doc=7623,freq=2.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.20383182 = fieldWeight in 7623, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.125 = fieldNorm(doc=7623)
  0.4 = coord(2/5)
    Source
    IEEE expert. 8(1993) no.2, S.25-34
    Type
    a
  13. Chen, H.; Lynch, K.J.; Bashu, K.; Ng, T.D.: Generating, integrating, and activating thesauri for concept-based document retrieval (1993) 0.01 
    0.0063148676 = product of:
  0.01578717 = sum of:
    0.00743023 = weight(_text_:s in 8549) [ClassicSimilarity], result of:
      0.00743023 = score(doc=8549,freq=2.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.19219826 = fieldWeight in 8549, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.125 = fieldNorm(doc=8549)
    0.00835694 = weight(_text_:a in 8549) [ClassicSimilarity], result of:
      0.00835694 = score(doc=8549,freq=2.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.20383182 = fieldWeight in 8549, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.125 = fieldNorm(doc=8549)
  0.4 = coord(2/5)
    Source
    IEEE expert. 8(1993), April, S.25-34
    Type
    a
  14. Schumaker, R.P.; Chen, H.: Evaluating a news-aware quantitative trader : the effect of momentum and contrarian stock selection strategies (2008) 0.01 
    0.006150737 = product of:
  0.015376842 = sum of:
    0.0032507253 = weight(_text_:s in 1352) [ClassicSimilarity], result of:
      0.0032507253 = score(doc=1352,freq=2.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.08408674 = fieldWeight in 1352, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1352)
    0.012126116 = weight(_text_:a in 1352) [ClassicSimilarity], result of:
      0.012126116 = score(doc=1352,freq=22.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.29576474 = fieldWeight in 1352, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1352)
  0.4 = coord(2/5)
    Abstract
    We study the coupling of basic quantitative portfolio selection strategies with a financial news article prediction system, AZFinText. By varying the degrees of portfolio formation time, we found that a hybrid system using both quantitative strategy and a full set of financial news articles performed the best. With a 1-week portfolio formation period, we achieved a 20.79% trading return using a Momentum strategy and a 4.54% return using a Contrarian strategy over a 5-week holding period. We also found that trader overreaction to these events led AZFinText to capitalize on these short-term surges in price.
    Source
    Journal of the American Society for Information Science and Technology. 59(2008) no.2, S.247-255
    Type
    a
  15. Chen, H.; Dhar, V.: Cognitive process as a basis for intelligent retrieval system design (1991) 0.00 
    0.004828822 = product of:
  0.012072055 = sum of:
    0.003715115 = weight(_text_:s in 3845) [ClassicSimilarity], result of:
      0.003715115 = score(doc=3845,freq=2.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.09609913 = fieldWeight in 3845, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0625 = fieldNorm(doc=3845)
    0.00835694 = weight(_text_:a in 3845) [ClassicSimilarity], result of:
      0.00835694 = score(doc=3845,freq=8.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.20383182 = fieldWeight in 3845, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0625 = fieldNorm(doc=3845)
  0.4 = coord(2/5)
    Abstract
    2 studies were conducted to investigate the cognitive processes involved in online document-based information retrieval. These studies led to the development of 5 computerised models of online document retrieval. These models were incorporated into a design of an 'intelligent' document-based retrieval system. Following a discussion of this system, discusses the broader implications of the research for the design of information retrieval sysems
    Source
    Information processing and management. 27(1991) no.5, S.405-432
    Type
    a
  16. Chen, H.: Explaining and alleviating information management indeterminism : a knowledge-based framework (1994) 0.00 
    0.004828822 = product of:
  0.012072055 = sum of:
    0.003715115 = weight(_text_:s in 8221) [ClassicSimilarity], result of:
      0.003715115 = score(doc=8221,freq=2.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.09609913 = fieldWeight in 8221, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0625 = fieldNorm(doc=8221)
    0.00835694 = weight(_text_:a in 8221) [ClassicSimilarity], result of:
      0.00835694 = score(doc=8221,freq=8.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.20383182 = fieldWeight in 8221, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0625 = fieldNorm(doc=8221)
  0.4 = coord(2/5)
    Abstract
    Attempts to identify the nature and causes of information management indeterminism in an online research environment and proposes solutions for alleviating this indeterminism. Conducts two empirical studies of information management activities. The first identified the types and nature of information management indeterminism by evaluating archived text. The second focused on four sources of indeterminism: subject area knowledge, classification knowledge, system knowledge, and collaboration knowledge. Proposes a knowledge based design for alleviating indeterminism, which contains a system generated thesaurus and an inferencing engine
    Source
    Information processing and management. 30(1994) no.4, S.557-577
    Type
    a
  17. Chen, H.: ¬An analysis of image queries in the field of art history (2001) 0.00 
    0.0047638174 = product of:
  0.011909544 = sum of:
    0.0045972206 = weight(_text_:s in 5187) [ClassicSimilarity], result of:
      0.0045972206 = score(doc=5187,freq=4.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.118916616 = fieldWeight in 5187, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5187)
    0.007312323 = weight(_text_:a in 5187) [ClassicSimilarity], result of:
      0.007312323 = score(doc=5187,freq=8.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.17835285 = fieldWeight in 5187, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5187)
  0.4 = coord(2/5)
    Abstract
    Chen arranged with an Art History instructor to require 20 medieval art images in papers received from 29 students. Participants completed a self administered presearch and postsearch questionnaire, and were interviewed after questionnaire analysis, in order to collect both the keywords and phrases they planned to use, and those actually used. Three MLIS student reviewers then mapped the queries to Enser and McGregor's four categories, Jorgensen's 12 classes, and Fidel's 12 feature data and object poles providing a degree of match on a seven point scale (one not at all to 7 exact). The reviewers give highest scores to Enser and McGregor;'s categories. Modifications to both the Enser and McGregor and Jorgensen schemes are suggested
    Source
    Journal of the American Society for Information Science and technology. 52(2001) no.3, S.260-273
    Type
    a
  18. Chen, H.; Ng, T.: ¬An algorithmic approach to concept exploration in a large knowledge network (automatic thesaurus consultation) : symbolic branch-and-bound search versus connectionist Hopfield Net Activation (1995) 0.00 
    0.0046600844 = product of:
  0.01165021 = sum of:
    0.002786336 = weight(_text_:s in 2203) [ClassicSimilarity], result of:
      0.002786336 = score(doc=2203,freq=2.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.072074346 = fieldWeight in 2203, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.046875 = fieldNorm(doc=2203)
    0.008863874 = weight(_text_:a in 2203) [ClassicSimilarity], result of:
      0.008863874 = score(doc=2203,freq=16.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.2161963 = fieldWeight in 2203, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=2203)
  0.4 = coord(2/5)
    Abstract
    Presents a framework for knowledge discovery and concept exploration. In order to enhance the concept exploration capability of knowledge based systems and to alleviate the limitation of the manual browsing approach, develops 2 spreading activation based algorithms for concept exploration in large, heterogeneous networks of concepts (eg multiple thesauri). One algorithm, which is based on the symbolic AI paradigma, performs a conventional branch-and-bound search on a semantic net representation to identify other highly relevant concepts (a serial, optimal search process). The 2nd algorithm, which is absed on the neural network approach, executes the Hopfield net parallel relaxation and convergence process to identify 'convergent' concepts for some initial queries (a parallel, heuristic search process). Tests these 2 algorithms on a large text-based knowledge network of about 13.000 nodes (terms) and 80.000 directed links in the area of computing technologies
    Source
    Journal of the American Society for Information Science. 46(1995) no.5, S.348-369
    Type
    a
  19. Orwig, R.E.; Chen, H.; Nunamaker, J.F.: ¬A graphical, self-organizing approach to classifying electronic meeting output (1997) 0.00 
    0.0045704604 = product of:
  0.011426151 = sum of:
    0.0032507253 = weight(_text_:s in 6928) [ClassicSimilarity], result of:
      0.0032507253 = score(doc=6928,freq=2.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.08408674 = fieldWeight in 6928, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0546875 = fieldNorm(doc=6928)
    0.008175425 = weight(_text_:a in 6928) [ClassicSimilarity], result of:
      0.008175425 = score(doc=6928,freq=10.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.19940455 = fieldWeight in 6928, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=6928)
  0.4 = coord(2/5)
    Abstract
    Describes research in the application of a Kohonen Self-Organizing Map (SOM) to the problem of classification of electronic brainstorming output and an evaluation of the results. Describes an electronic meeting system and describes the classification problem that exists in the group problem solving process. Surveys the literature concerning classification. Describes the application of the Kohonen SOM to the meeting output classification problem. Describes an experiment that evaluated the classification performed by the Kohonen SOM by comparing it with those of a human expert and a Hopfield neural network. Discusses conclusions and directions for future research
    Source
    Journal of the American Society for Information Science. 48(1997) no.2, S.157-170
    Type
    a
  20. Li, J.; Zhang, Z.; Li, X.; Chen, H.: Kernel-based learning for biomedical relation extraction (2008) 0.00 
    0.0041850703 = product of:
  0.010462675 = sum of:
    0.002786336 = weight(_text_:s in 1611) [ClassicSimilarity], result of:
      0.002786336 = score(doc=1611,freq=2.0), product of:
        0.038659193 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.035557263 = queryNorm
        0.072074346 = fieldWeight in 1611, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.046875 = fieldNorm(doc=1611)
    0.0076763397 = weight(_text_:a in 1611) [ClassicSimilarity], result of:
      0.0076763397 = score(doc=1611,freq=12.0), product of:
        0.040999193 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.035557263 = queryNorm
        0.18723148 = fieldWeight in 1611, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=1611)
  0.4 = coord(2/5)
    Abstract
    Relation extraction is the process of scanning text for relationships between named entities. Recently, significant studies have focused on automatically extracting relations from biomedical corpora. Most existing biomedical relation extractors require manual creation of biomedical lexicons or parsing templates based on domain knowledge. In this study, we propose to use kernel-based learning methods to automatically extract biomedical relations from literature text. We develop a framework of kernel-based learning for biomedical relation extraction. In particular, we modified the standard tree kernel function by incorporating a trace kernel to capture richer contextual information. In our experiments on a biomedical corpus, we compare different kernel functions for biomedical relation detection and classification. The experimental results show that a tree kernel outperforms word and sequence kernels for relation detection, our trace-tree kernel outperforms the standard tree kernel, and a composite kernel outperforms individual kernels for relation extraction.
    Source
    Journal of the American Society for Information Science and Technology. 59(2008) no.5, S.756-769
    Type
    a

Authors

Years

Types

  • a 61
  • el 1
  • More… Less…

Themes