Search (4 results, page 1 of 1)

  • × author_ss:"Yang, C.C."
  1. Chau, M.; Lu, Y.; Fang, X.; Yang, C.C.: Characteristics of character usage in Chinese Web searching (2009) 0.13
    0.13408904 = product of:
      0.33522257 = sum of:
        0.30227408 = weight(_text_:grams in 2456) [ClassicSimilarity], result of:
          0.30227408 = score(doc=2456,freq=6.0), product of:
            0.39198354 = queryWeight, product of:
              8.059301 = idf(docFreq=37, maxDocs=44218)
              0.04863741 = queryNorm
            0.77113974 = fieldWeight in 2456, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              8.059301 = idf(docFreq=37, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2456)
        0.032948487 = weight(_text_:22 in 2456) [ClassicSimilarity], result of:
          0.032948487 = score(doc=2456,freq=2.0), product of:
            0.17031991 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04863741 = queryNorm
            0.19345059 = fieldWeight in 2456, 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=2456)
      0.4 = coord(2/5)
    
    Abstract
    The use of non-English Web search engines has been prevalent. Given the popularity of Chinese Web searching and the unique characteristics of Chinese language, it is imperative to conduct studies with focuses on the analysis of Chinese Web search queries. In this paper, we report our research on the character usage of Chinese search logs from a Web search engine in Hong Kong. By examining the distribution of search query terms, we found that users tended to use more diversified terms and that the usage of characters in search queries was quite different from the character usage of general online information in Chinese. After studying the Zipf distribution of n-grams with different values of n, we found that the curve of unigram is the most curved one of all while the bigram curve follows the Zipf distribution best, and that the curves of n-grams with larger n (n = 3-6) had similar structures with ?-values in the range of 0.66-0.86. The distribution of combined n-grams was also studied. All the analyses are performed on the data both before and after the removal of function terms and incomplete terms and similar findings are revealed. We believe the findings from this study have provided some insights into further research in non-English Web searching and will assist in the design of more effective Chinese Web search engines.
    Date
    22.11.2008 17:57:22
  2. Yang, C.C.; Li, K.W.: ¬A heuristic method based on a statistical approach for chinese text segmentation (2005) 0.05
    0.04936115 = product of:
      0.24680576 = sum of:
        0.24680576 = weight(_text_:grams in 4580) [ClassicSimilarity], result of:
          0.24680576 = score(doc=4580,freq=4.0), product of:
            0.39198354 = queryWeight, product of:
              8.059301 = idf(docFreq=37, maxDocs=44218)
              0.04863741 = queryNorm
            0.62963295 = fieldWeight in 4580, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              8.059301 = idf(docFreq=37, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4580)
      0.2 = coord(1/5)
    
    Abstract
    The authors propose a heuristic method for Chinese automatic text segmentation based an a statistical approach. This method is developed based an statistical information about the association among adjacent characters in Chinese text. Mutual information of bi-grams and significant estimation of tri-grams are utilized. A heuristic method with six rules is then proposed to determine the segmentation points in a Chinese sentence. No dictionary is required in this method. Chinese text segmentation is important in Chinese text indexing and thus greatly affects the performance of Chinese information retrieval. Due to the lack of delimiters of words in Chinese text, Chinese text segmentation is more difficult than English text segmentation. Besides, segmentation ambiguities and occurrences of out-of-vocabulary words (i.e., unknown words) are the major challenges in Chinese segmentation. Many research studies dealing with the problem of word segmentation have focused an the resolution of segmentation ambiguities. The problem of unknown word identification has not drawn much attention. The experimental result Shows that the proposed heuristic method is promising to segment the unknown words as weIl as the known words. The authors further investigated the distribution of the errors of commission and the errors of omission caused by the proposed heuristic method and benchmarked the proposed heuristic method with a previous proposed technique, boundary detection. It is found that the heuristic method outperformed the boundary detection method.
  3. Yang, C.C.; Liu, N.: Web site topic-hierarchy generation based on link structure (2009) 0.01
    0.006589697 = product of:
      0.032948487 = sum of:
        0.032948487 = weight(_text_:22 in 2738) [ClassicSimilarity], result of:
          0.032948487 = score(doc=2738,freq=2.0), product of:
            0.17031991 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04863741 = queryNorm
            0.19345059 = fieldWeight in 2738, 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=2738)
      0.2 = coord(1/5)
    
    Date
    22. 3.2009 12:51:47
  4. Yang, C.C.; Luk, J.: Automatic generation of English/Chinese thesaurus based on a parallel corpus in laws (2003) 0.00
    0.004612788 = product of:
      0.02306394 = sum of:
        0.02306394 = weight(_text_:22 in 1616) [ClassicSimilarity], result of:
          0.02306394 = score(doc=1616,freq=2.0), product of:
            0.17031991 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04863741 = queryNorm
            0.1354154 = fieldWeight in 1616, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02734375 = fieldNorm(doc=1616)
      0.2 = coord(1/5)
    
    Abstract
    The information available in languages other than English in the World Wide Web is increasing significantly. According to a report from Computer Economics in 1999, 54% of Internet users are English speakers ("English Will Dominate Web for Only Three More Years," Computer Economics, July 9, 1999, http://www.computereconomics. com/new4/pr/pr990610.html). However, it is predicted that there will be only 60% increase in Internet users among English speakers verses a 150% growth among nonEnglish speakers for the next five years. By 2005, 57% of Internet users will be non-English speakers. A report by CNN.com in 2000 showed that the number of Internet users in China had been increased from 8.9 million to 16.9 million from January to June in 2000 ("Report: China Internet users double to 17 million," CNN.com, July, 2000, http://cnn.org/2000/TECH/computing/07/27/ china.internet.reut/index.html). According to Nielsen/ NetRatings, there was a dramatic leap from 22.5 millions to 56.6 millions Internet users from 2001 to 2002. China had become the second largest global at-home Internet population in 2002 (US's Internet population was 166 millions) (Robyn Greenspan, "China Pulls Ahead of Japan," Internet.com, April 22, 2002, http://cyberatias.internet.com/big-picture/geographics/article/0,,5911_1013841,00. html). All of the evidences reveal the importance of crosslingual research to satisfy the needs in the near future. Digital library research has been focusing in structural and semantic interoperability in the past. Searching and retrieving objects across variations in protocols, formats and disciplines are widely explored (Schatz, B., & Chen, H. (1999). Digital libraries: technological advances and social impacts. IEEE Computer, Special Issue an Digital Libraries, February, 32(2), 45-50.; Chen, H., Yen, J., & Yang, C.C. (1999). International activities: development of Asian digital libraries. IEEE Computer, Special Issue an Digital Libraries, 32(2), 48-49.). However, research in crossing language boundaries, especially across European languages and Oriental languages, is still in the initial stage. In this proposal, we put our focus an cross-lingual semantic interoperability by developing automatic generation of a cross-lingual thesaurus based an English/Chinese parallel corpus. When the searchers encounter retrieval problems, Professional librarians usually consult the thesaurus to identify other relevant vocabularies. In the problem of searching across language boundaries, a cross-lingual thesaurus, which is generated by co-occurrence analysis and Hopfield network, can be used to generate additional semantically relevant terms that cannot be obtained from dictionary. In particular, the automatically generated cross-lingual thesaurus is able to capture the unknown words that do not exist in a dictionary, such as names of persons, organizations, and events. Due to Hong Kong's unique history background, both English and Chinese are used as official languages in all legal documents. Therefore, English/Chinese cross-lingual information retrieval is critical for applications in courts and the government. In this paper, we develop an automatic thesaurus by the Hopfield network based an a parallel corpus collected from the Web site of the Department of Justice of the Hong Kong Special Administrative Region (HKSAR) Government. Experiments are conducted to measure the precision and recall of the automatic generated English/Chinese thesaurus. The result Shows that such thesaurus is a promising tool to retrieve relevant terms, especially in the language that is not the same as the input term. The direct translation of the input term can also be retrieved in most of the cases.