Search (5 results, page 1 of 1)

0.06878099 = product of:
  0.13756198 = sum of:
    0.13756198 = sum of:
      0.10297343 = weight(_text_:engines in 2456) [ClassicSimilarity], result of:
        0.10297343 = score(doc=2456,freq=4.0), product of:
          0.25941864 = queryWeight, product of:
            5.080822 = idf(docFreq=746, maxDocs=44218)
            0.051058397 = queryNorm
          0.39693922 = fieldWeight in 2456, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            5.080822 = idf(docFreq=746, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2456)
      0.03458854 = weight(_text_:22 in 2456) [ClassicSimilarity], result of:
        0.03458854 = score(doc=2456,freq=2.0), product of:
          0.17879781 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.051058397 = 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)
  0.5 = coord(1/2)

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

0.031529047 = product of:
  0.06305809 = sum of:
    0.06305809 = product of:
      0.12611619 = sum of:
        0.12611619 = weight(_text_:engines in 597) [ClassicSimilarity], result of:
          0.12611619 = score(doc=597,freq=6.0), product of:
            0.25941864 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.051058397 = queryNorm
            0.4861493 = fieldWeight in 597, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.0390625 = fieldNorm(doc=597)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

With the overwhelming volume of information, the task of finding relevant information on a given topic on the Web is becoming increasingly difficult. Web search engines hence become one of the most popular solutions available on the Web. However, it has never been easy for novice users to organize and represent their information needs using simple queries. Users have to keep modifying their input queries until they get expected results. Therefore, it is often desirable for search engines to give suggestions on related queries to users. Besides, by identifying those related queries, search engines can potentially perform optimizations on their systems, such as query expansion and file indexing. In this work we propose a method that suggests a list of related queries given an initial input query. The related queries are based in the query log of previously submitted queries by human users, which can be identified using an enhanced model of association rules. Users can utilize the suggested related queries to tune or redirect the search process. Our method not only discovers the related queries, but also ranks them according to the degree of their relatedness. Unlike many other rival techniques, it also performs reasonably well on less frequent input queries.

0.025743358 = product of:
  0.051486716 = sum of:
    0.051486716 = product of:
      0.10297343 = sum of:
        0.10297343 = weight(_text_:engines in 604) [ClassicSimilarity], result of:
          0.10297343 = score(doc=604,freq=4.0), product of:
            0.25941864 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.051058397 = queryNorm
            0.39693922 = fieldWeight in 604, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.0390625 = fieldNorm(doc=604)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Modern information retrieval systems use keywords within documents as indexing terms for search of relevant documents. As Chinese is an ideographic character-based language, the words in the texts are not delimited by white spaces. Indexing of Chinese documents is impossible without a proper segmentation algorithm. Many Chinese segmentation algorithms have been proposed in the past. Traditional segmentation algorithms cannot operate without a large dictionary or a large corpus of training data. Nowadays, the Web has become the largest corpus that is ideal for Chinese segmentation. Although most search engines have problems in segmenting texts into proper words, they maintain huge databases of documents and frequencies of character sequences in the documents. Their databases are important potential resources for segmentation. In this paper, we propose a segmentation algorithm by mining Web data with the help of search engines. On the other hand, the Romanized pinyin of Chinese language indicates boundaries of words in the text. Our algorithm is the first to utilize the Romanized pinyin to segmentation. It is the first unified segmentation algorithm for the Chinese language from different geographical areas, and it is also domain independent because of the nature of the Web. Experiments have been conducted on the datasets of a recent Chinese segmentation competition. The results show that our algorithm outperforms the traditional algorithms in terms of precision and recall. Moreover, our algorithm can effectively deal with the problems of segmentation ambiguity, new word (unknown word) detection, and stop words.

0.008647135 = product of:
  0.01729427 = sum of:
    0.01729427 = product of:
      0.03458854 = sum of:
        0.03458854 = weight(_text_:22 in 2738) [ClassicSimilarity], result of:
          0.03458854 = score(doc=2738,freq=2.0), product of:
            0.17879781 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051058397 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 3.2009 12:51:47

0.0060529946 = product of:
  0.012105989 = sum of:
    0.012105989 = product of:
      0.024211979 = sum of:
        0.024211979 = weight(_text_:22 in 1616) [ClassicSimilarity], result of:
          0.024211979 = score(doc=1616,freq=2.0), product of:
            0.17879781 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051058397 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.