Search (42 results, page 1 of 3)

  • × author_ss:"Chen, H."
  1. Dumais, S.; Chen, H.: Hierarchical classification of Web content (2000) 0.04 
    0.044837344 = product of:
  0.11209336 = sum of:
    0.07941282 = weight(_text_:retrieval in 492) [ClassicSimilarity], result of:
      0.07941282 = score(doc=492,freq=4.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.5671716 = fieldWeight in 492, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.09375 = fieldNorm(doc=492)
    0.03268054 = product of:
      0.06536108 = sum of:
        0.06536108 = weight(_text_:web in 492) [ClassicSimilarity], result of:
          0.06536108 = score(doc=492,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.43268442 = fieldWeight in 492, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.09375 = fieldNorm(doc=492)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    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
    Theme
    Klassifikationssysteme im Online-Retrieval
  2. Chen, H.; Zhang, Y.; Houston, A.L.: Semantic indexing and searching using a Hopfield net (1998) 0.04 
    0.041238464 = product of:
  0.10309616 = sum of:
    0.028076671 = weight(_text_:retrieval in 5704) [ClassicSimilarity], result of:
      0.028076671 = score(doc=5704,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.20052543 = fieldWeight in 5704, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=5704)
    0.075019486 = weight(_text_:semantic in 5704) [ClassicSimilarity], result of:
      0.075019486 = score(doc=5704,freq=4.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.38979942 = fieldWeight in 5704, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.046875 = fieldNorm(doc=5704)
  0.4 = coord(2/5)
    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
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  3. Chen, H.: Introduction to the JASIST special topic section on Web retrieval and mining : A machine learning perspective (2003) 0.03 
    0.034067273 = product of:
  0.08516818 = sum of:
    0.048630223 = weight(_text_:retrieval in 1610) [ClassicSimilarity], result of:
      0.048630223 = score(doc=1610,freq=6.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.34732026 = fieldWeight in 1610, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=1610)
    0.03653796 = product of:
      0.07307592 = sum of:
        0.07307592 = weight(_text_:web in 1610) [ClassicSimilarity], result of:
          0.07307592 = score(doc=1610,freq=10.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.48375595 = fieldWeight in 1610, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.046875 = fieldNorm(doc=1610)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Abstract
    Research in information retrieval (IR) has advanced significantly in the past few decades. Many tasks, such as indexing and text categorization, can be performed automatically with minimal human effort. Machine learning has played an important role in such automation by learning various patterns such as document topics, text structures, and user interests from examples. In recent years, it has become increasingly difficult to search for useful information an the World Wide Web because of its large size and unstructured nature. Useful information and resources are often hidden in the Web. While machine learning has been successfully applied to traditional IR systems, it poses some new challenges to apply these algorithms to the Web due to its large size, link structure, diversity in content and languages, and dynamic nature. On the other hand, such characteristics of the Web also provide interesting patterns and knowledge that do not present in traditional information retrieval systems.
  4. Chen, H.: Semantic research for digital libraries (1999) 0.03 
    0.032449383 = product of:
  0.08112346 = sum of:
    0.028076671 = weight(_text_:retrieval in 1247) [ClassicSimilarity], result of:
      0.028076671 = score(doc=1247,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.20052543 = fieldWeight in 1247, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=1247)
    0.05304678 = weight(_text_:semantic in 1247) [ClassicSimilarity], result of:
      0.05304678 = score(doc=1247,freq=2.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.2756298 = fieldWeight in 1247, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.046875 = fieldNorm(doc=1247)
  0.4 = coord(2/5)
    Abstract
    In this era of the Internet and distributed, multimedia computing, new and emerging classes of information systems applications have swept into the lives of office workers and people in general. From digital libraries, multimedia systems, geographic information systems, and collaborative computing to electronic commerce, virtual reality, and electronic video arts and games, these applications have created tremendous opportunities for information and computer science researchers and practitioners. As applications become more pervasive, pressing, and diverse, several well-known information retrieval (IR) problems have become even more urgent. Information overload, a result of the ease of information creation and transmission via the Internet and WWW, has become more troublesome (e.g., even stockbrokers and elementary school students, heavily exposed to various WWW search engines, are versed in such IR terminology as recall and precision). Significant variations in database formats and structures, the richness of information media (text, audio, and video), and an abundance of multilingual information content also have created severe information interoperability problems -- structural interoperability, media interoperability, and multilingual interoperability.
  5. Qin, J.; Zhou, Y.; Chau, M.; Chen, H.: Multilingual Web retrieval : an experiment in English-Chinese business intelligence (2006) 0.03 
    0.032059558 = product of:
  0.08014889 = sum of:
    0.04679445 = weight(_text_:retrieval in 5054) [ClassicSimilarity], result of:
      0.04679445 = score(doc=5054,freq=8.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.33420905 = fieldWeight in 5054, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5054)
    0.03335444 = product of:
      0.06670888 = sum of:
        0.06670888 = weight(_text_:web in 5054) [ClassicSimilarity], result of:
          0.06670888 = score(doc=5054,freq=12.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.4416067 = fieldWeight in 5054, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5054)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Abstract
    As increasing numbers of non-English resources have become available on the Web, the interesting and important issue of how Web users can retrieve documents in different languages has arisen. Cross-language information retrieval (CLIP), the study of retrieving information in one language by queries expressed in another language, is a promising approach to the problem. Cross-language information retrieval has attracted much attention in recent years. Most research systems have achieved satisfactory performance on standard Text REtrieval Conference (TREC) collections such as news articles, but CLIR techniques have not been widely studied and evaluated for applications such as Web portals. In this article, the authors present their research in developing and evaluating a multilingual English-Chinese Web portal that incorporates various CLIP techniques for use in the business domain. A dictionary-based approach was adopted and combines phrasal translation, co-occurrence analysis, and pre- and posttranslation query expansion. The portal was evaluated by domain experts, using a set of queries in both English and Chinese. The experimental results showed that co-occurrence-based phrasal translation achieved a 74.6% improvement in precision over simple word-byword translation. When used together, pre- and posttranslation query expansion improved the performance slightly, achieving a 78.0% improvement over the baseline word-by-word translation approach. In general, applying CLIR techniques in Web applications shows promise.
  6. Chung, W.; Chen, H.: Browsing the underdeveloped Web : an experiment on the Arabic Medical Web Directory (2009) 0.03 
    0.031091845 = product of:
  0.15545923 = sum of:
    0.15545923 = sum of:
      0.11783138 = weight(_text_:web in 2733) [ClassicSimilarity], result of:
        0.11783138 = score(doc=2733,freq=26.0), product of:
          0.15105948 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.04628742 = queryNorm
          0.780033 = fieldWeight in 2733, product of:
            5.0990195 = tf(freq=26.0), with freq of:
              26.0 = termFreq=26.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.046875 = fieldNorm(doc=2733)
      0.03762784 = weight(_text_:22 in 2733) [ClassicSimilarity], result of:
        0.03762784 = score(doc=2733,freq=2.0), product of:
          0.16209066 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.04628742 = 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)
  0.2 = coord(1/5)
    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
  7. Huang, Z.; Chung, Z.W.; Chen, H.: ¬A graph model for e-commerce recommender systems (2004) 0.03 
    0.028997445 = product of:
  0.07249361 = sum of:
    0.056153342 = weight(_text_:retrieval in 501) [ClassicSimilarity], result of:
      0.056153342 = score(doc=501,freq=8.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.40105087 = fieldWeight in 501, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=501)
    0.01634027 = product of:
      0.03268054 = sum of:
        0.03268054 = weight(_text_:web in 501) [ClassicSimilarity], result of:
          0.03268054 = score(doc=501,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.21634221 = fieldWeight in 501, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.046875 = fieldNorm(doc=501)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Abstract
    Information overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customers' preferences efficiently. Various recommender systems employing different data representations and recommendation methods are currently used to address these challenges. In this research, we developed a graph model that provides a generic data representation and can support different recommendation methods. To demonstrate its usefulness and flexibility, we developed three recommendation methods: direct retrieval, association mining, and high-degree association retrieval. We used a data set from an online bookstore as our research test-bed. Evaluation results showed that combining product content information and historical customer transaction information achieved more accurate predictions and relevant recommendations than using only collaborative information. However, comparisons among different methods showed that high-degree association retrieval did not perform significantly better than the association mining method or the direct retrieval method in our test-bed.
  8. Chen, H.; Lally, A.M.; Zhu, B.; Chau, M.: HelpfulMed : Intelligent searching for medical information over the Internet (2003) 0.03 
    0.027103592 = product of:
  0.06775898 = sum of:
    0.040525187 = weight(_text_:retrieval in 1615) [ClassicSimilarity], result of:
      0.040525187 = score(doc=1615,freq=6.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.28943354 = fieldWeight in 1615, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1615)
    0.027233787 = product of:
      0.054467574 = sum of:
        0.054467574 = weight(_text_:web in 1615) [ClassicSimilarity], result of:
          0.054467574 = score(doc=1615,freq=8.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.36057037 = fieldWeight in 1615, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1615)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    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"
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  9. Fu, T.; Abbasi, A.; Chen, H.: ¬A focused crawler for Dark Web forums (2010) 0.03 
    0.025699163 = product of:
  0.064247906 = sum of:
    0.023397226 = weight(_text_:retrieval in 3471) [ClassicSimilarity], result of:
      0.023397226 = score(doc=3471,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.16710453 = fieldWeight in 3471, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3471)
    0.040850677 = product of:
      0.08170135 = sum of:
        0.08170135 = weight(_text_:web in 3471) [ClassicSimilarity], result of:
          0.08170135 = score(doc=3471,freq=18.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.5408555 = fieldWeight in 3471, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3471)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Abstract
    The unprecedented growth of the Internet has given rise to the Dark Web, the problematic facet of the Web associated with cybercrime, hate, and extremism. Despite the need for tools to collect and analyze Dark Web forums, the covert nature of this part of the Internet makes traditional Web crawling techniques insufficient for capturing such content. In this study, we propose a novel crawling system designed to collect Dark Web forum content. The system uses a human-assisted accessibility approach to gain access to Dark Web forums. Several URL ordering features and techniques enable efficient extraction of forum postings. The system also includes an incremental crawler coupled with a recall-improvement mechanism intended to facilitate enhanced retrieval and updating of collected content. Experiments conducted to evaluate the effectiveness of the human-assisted accessibility approach and the recall-improvement-based, incremental-update procedure yielded favorable results. The human-assisted approach significantly improved access to Dark Web forums while the incremental crawler with recall improvement also outperformed standard periodic- and incremental-update approaches. Using the system, we were able to collect over 100 Dark Web forums from three regions. A case study encompassing link and content analysis of collected forums was used to illustrate the value and importance of gathering and analyzing content from such online communities.
  10. Leroy, G.; Chen, H.: Genescene: an ontology-enhanced integration of linguistic and co-occurrence based relations in biomedical texts (2005) 0.02 
    0.023953566 = product of:
  0.059883915 = sum of:
    0.04420565 = weight(_text_:semantic in 5259) [ClassicSimilarity], result of:
      0.04420565 = score(doc=5259,freq=2.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.22969149 = fieldWeight in 5259, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5259)
    0.015678266 = product of:
      0.031356532 = sum of:
        0.031356532 = weight(_text_:22 in 5259) [ClassicSimilarity], result of:
          0.031356532 = score(doc=5259,freq=2.0), product of:
            0.16209066 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04628742 = 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)
  0.4 = coord(2/5)
    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
  11. Chen, H.; Dhar, V.: Cognitive process as a basis for intelligent retrieval system design (1991) 0.02 
    0.016741691 = product of:
  0.08370846 = sum of:
    0.08370846 = weight(_text_:retrieval in 3845) [ClassicSimilarity], result of:
      0.08370846 = score(doc=3845,freq=10.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.59785134 = fieldWeight in 3845, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0625 = fieldNorm(doc=3845)
  0.2 = coord(1/5)
    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
  12. Chen, H.: Knowledge-based document retrieval : framework and design (1992) 0.01 
    0.014974224 = product of:
  0.07487112 = sum of:
    0.07487112 = weight(_text_:retrieval in 5283) [ClassicSimilarity], result of:
      0.07487112 = score(doc=5283,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.5347345 = fieldWeight in 5283, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.125 = fieldNorm(doc=5283)
  0.2 = coord(1/5)
  13. Chen, H.: Generating, integrating and activating thesauri for concept-based document retrieval (1993) 0.01 
    0.014974224 = product of:
  0.07487112 = sum of:
    0.07487112 = weight(_text_:retrieval in 7623) [ClassicSimilarity], result of:
      0.07487112 = score(doc=7623,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.5347345 = fieldWeight in 7623, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.125 = fieldNorm(doc=7623)
  0.2 = coord(1/5)
  14. Chen, H.; Lynch, K.J.; Bashu, K.; Ng, T.D.: Generating, integrating, and activating thesauri for concept-based document retrieval (1993) 0.01 
    0.014974224 = product of:
  0.07487112 = sum of:
    0.07487112 = weight(_text_:retrieval in 8549) [ClassicSimilarity], result of:
      0.07487112 = score(doc=8549,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.5347345 = fieldWeight in 8549, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.125 = fieldNorm(doc=8549)
  0.2 = coord(1/5)
  15. Zhu, B.; Chen, H.: Validating a geographical image retrieval system (2000) 0.01 
    0.013754703 = product of:
  0.068773516 = sum of:
    0.068773516 = weight(_text_:retrieval in 4769) [ClassicSimilarity], result of:
      0.068773516 = score(doc=4769,freq=12.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.49118498 = fieldWeight in 4769, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=4769)
  0.2 = coord(1/5)
    Abstract
    This paper summarizes a prototype geographical image retrieval system that demonstrates how to integrate image processing and information analysis techniques to support large-scale content-based image retrieval. By using an image as its interface, the prototype system addresses a troublesome aspect of traditional retrieval models, which require users to have complete knowledge of the low-level features of an image. In addition we describe an experiment to validate against that of human subjects in an effort to address the scarcity of research evaluating performance of an algorithm against that of human beings. The results of the experiment indicate that the system could do as well as human subjects in accomplishing the tasks of similarity analysis and image categorization. We also found that under some circumstances texture features of an image are insufficient to represent an geographic image. We believe, however, that our image retrieval system provides a promising approach to integrating image processing techniques and information retrieval algorithms
  16. 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) 0.01 
    0.013235469 = product of:
  0.066177346 = sum of:
    0.066177346 = weight(_text_:retrieval in 6417) [ClassicSimilarity], result of:
      0.066177346 = score(doc=6417,freq=4.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.47264296 = fieldWeight in 6417, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.078125 = fieldNorm(doc=6417)
  0.2 = coord(1/5)
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  17. Chen, H.: Machine learning for information retrieval : neural networks, symbolic learning, and genetic algorithms (1994) 0.01 
    0.013102447 = product of:
  0.06551223 = sum of:
    0.06551223 = weight(_text_:retrieval in 2657) [ClassicSimilarity], result of:
      0.06551223 = score(doc=2657,freq=8.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.46789268 = fieldWeight in 2657, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2657)
  0.2 = coord(1/5)
    Abstract
    In the 1980s, knowledge-based techniques also made an impressive contribution to 'intelligent' information retrieval and indexing. More recently, researchers have turned to newer artificial intelligence based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms grounded on diverse paradigms. These have provided great opportunities to enhance the capabilities of current information storage and retrieval systems. Provides an overview of these techniques and presents 3 popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evaluation based genetic algorithms in the context of information retrieval. The techniques are promising in their ability to analyze user queries, identify users' information needs, and suggest alternatives for search and can greatly complement the prevailing full text, keyword based, probabilistic, and knowledge based techniques
  18. Chen, H.; Shankaranarayanan, G.; She, L.: ¬A machine learning approach to inductive query by examples : an experiment using relevance feedback, ID3, genetic algorithms, and simulated annealing (1998) 0.01 
    0.01255627 = product of:
  0.06278135 = sum of:
    0.06278135 = weight(_text_:retrieval in 1148) [ClassicSimilarity], result of:
      0.06278135 = score(doc=1148,freq=10.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.44838852 = fieldWeight in 1148, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=1148)
  0.2 = coord(1/5)
    Abstract
    Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to 'intelligent' information retrieval and indexing. More recently, information science researchers have tfurned to other newer inductive learning techniques including symbolic learning, genetic algorithms, and simulated annealing. These newer techniques, which are grounded in diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information systems. In this article, we first provide an overview of these newer techniques and their use in information retrieval research. In order to femiliarize readers with the techniques, we present 3 promising methods: the symbolic ID3 algorithm, evolution-based genetic algorithms, and simulated annealing. We discuss their knowledge representations and algorithms in the unique context of information retrieval
  19. Hu, D.; Kaza, S.; Chen, H.: Identifying significant facilitators of dark network evolution (2009) 0.01 
    0.011718064 = product of:
  0.05859032 = sum of:
    0.05859032 = sum of:
      0.027233787 = weight(_text_:web in 2753) [ClassicSimilarity], result of:
        0.027233787 = score(doc=2753,freq=2.0), product of:
          0.15105948 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.04628742 = queryNorm
          0.18028519 = fieldWeight in 2753, 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=2753)
      0.031356532 = weight(_text_:22 in 2753) [ClassicSimilarity], result of:
        0.031356532 = score(doc=2753,freq=2.0), product of:
          0.16209066 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.04628742 = 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)
  0.2 = coord(1/5)
    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
  20. 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) 0.01 
    0.010609357 = product of:
  0.05304678 = sum of:
    0.05304678 = weight(_text_:semantic in 2203) [ClassicSimilarity], result of:
      0.05304678 = score(doc=2203,freq=2.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.2756298 = fieldWeight in 2203, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.046875 = fieldNorm(doc=2203)
  0.2 = coord(1/5)
    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

Authors

Years

Types

  • a 42
  • el 1
  • More… Less…

Themes