Search (11 results, page 1 of 1)

0.049989868 = product of:
  0.099979736 = sum of:
    0.02586502 = weight(_text_:data in 2456) [ClassicSimilarity], result of:
      0.02586502 = score(doc=2456,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.17468026 = fieldWeight in 2456, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2456)
    0.07411472 = sum of:
      0.042392377 = weight(_text_:processing in 2456) [ClassicSimilarity], result of:
        0.042392377 = score(doc=2456,freq=2.0), product of:
          0.18956426 = queryWeight, product of:
            4.048147 = idf(docFreq=2097, maxDocs=44218)
            0.046827413 = queryNorm
          0.22363065 = fieldWeight in 2456, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            4.048147 = idf(docFreq=2097, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2456)
      0.03172234 = weight(_text_:22 in 2456) [ClassicSimilarity], result of:
        0.03172234 = score(doc=2456,freq=2.0), product of:
          0.16398162 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046827413 = 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(2/4)

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

Source

Information processing and management. 45(2009) no.1, S.115-130

0.01293251 = product of:
  0.05173004 = sum of:
    0.05173004 = weight(_text_:data in 604) [ClassicSimilarity], result of:
      0.05173004 = score(doc=604,freq=8.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.34936053 = fieldWeight in 604, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=604)
  0.25 = coord(1/4)

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.

Theme

Data Mining

0.009052756 = product of:
  0.036211025 = sum of:
    0.036211025 = weight(_text_:data in 600) [ClassicSimilarity], result of:
      0.036211025 = score(doc=600,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.24455236 = fieldWeight in 600, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0546875 = fieldNorm(doc=600)
  0.25 = coord(1/4)

Theme

Data Mining

0.008959906 = product of:
  0.035839625 = sum of:
    0.035839625 = weight(_text_:data in 3391) [ClassicSimilarity], result of:
      0.035839625 = score(doc=3391,freq=6.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.24204408 = fieldWeight in 3391, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.03125 = fieldNorm(doc=3391)
  0.25 = coord(1/4)

Abstract

For the sake of national security, very large volumes of data and information are generated and gathered daily. Much of this data and information is written in different languages, stored in different locations, and may be seemingly unconnected. Crosslingual semantic interoperability is a major challenge to generate an overview of this disparate data and information so that it can be analyzed, shared, searched, and summarized. The recent terrorist attacks and the tragic events of September 11, 2001 have prompted increased attention an national security and criminal analysis. Many Asian countries and cities, such as Japan, Taiwan, and Singapore, have been advised that they may become the next targets of terrorist attacks. Semantic interoperability has been a focus in digital library research. Traditional information retrieval (IR) approaches normally require a document to share some common keywords with the query. Generating the associations for the related terms between the two term spaces of users and documents is an important issue. The problem can be viewed as the creation of a thesaurus. Apart from this, terrorists and criminals may communicate through letters, e-mails, and faxes in languages other than English. The translation ambiguity significantly exacerbates the retrieval problem. The problem is expanded to crosslingual semantic interoperability. In this paper, we focus an the English/Chinese crosslingual semantic interoperability problem. However, the developed techniques are not limited to English and Chinese languages but can be applied to many other languages. English and Chinese are popular languages in the Asian region. Much information about national security or crime is communicated in these languages. An efficient automatically generated thesaurus between these languages is important to crosslingual information retrieval between English and Chinese languages. To facilitate crosslingual information retrieval, a corpus-based approach uses the term co-occurrence statistics in parallel or comparable corpora to construct a statistical translation model to cross the language boundary. In this paper, the text based approach to align English/Chinese Hong Kong Police press release documents from the Web is first presented. We also introduce an algorithmic approach to generate a robust knowledge base based an statistical correlation analysis of the semantics (knowledge) embedded in the bilingual press release corpus. The research output consisted of a thesaurus-like, semantic network knowledge base, which can aid in semanticsbased crosslingual information management and retrieval.

0.006466255 = product of:
  0.02586502 = sum of:
    0.02586502 = weight(_text_:data in 597) [ClassicSimilarity], result of:
      0.02586502 = score(doc=597,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.17468026 = fieldWeight in 597, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=597)
  0.25 = coord(1/4)

Theme

Data Mining

0.006466255 = product of:
  0.02586502 = sum of:
    0.02586502 = weight(_text_:data in 744) [ClassicSimilarity], result of:
      0.02586502 = score(doc=744,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.17468026 = fieldWeight in 744, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=744)
  0.25 = coord(1/4)

Abstract

Interdisciplinary research has been attracting more attention in recent decades. In this article, we compare the similarity between scientific research domains and quantifying the temporal similarities of domains. We narrowed our study to three research domains: information retrieval (IR), database (DB), and World Wide Web (W3), because the rapid development of the W3 domain substantially attracted research efforts from both IR and DB domains and introduced new research questions to these two areas. Most existing approaches either employed a content-based technique or a cocitation or coauthorship network-based technique to study the development trend of a research area. In this work, we proposed an effective way to quantify the similarities among different research domains by incorporating content similarity and coauthorship network similarity. Experimental results on DBLP (DataBase systems and Logic Programming) data related to IR, DB, and W3 domains showed that the W3 domain was getting closer to both IR and DB whereas the distance between IR and DB remained relatively constant. In addition, comparing to IR and W3 with the DB domain, the DB domain was more conservative and evolved relatively slower.

0.006466255 = product of:
  0.02586502 = sum of:
    0.02586502 = weight(_text_:data in 1179) [ClassicSimilarity], result of:
      0.02586502 = score(doc=1179,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.17468026 = fieldWeight in 1179, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1179)
  0.25 = coord(1/4)

Abstract

E-patients seeking information online often seek specific advice related to coping with their health condition(s) among social networking sites. They may be looking for social connectivity with compassionate strangers who may have experienced similar situations to share opinions and experiences rather than for authoritative medical information. Previous studies document distinct technological features and different levels of social support interaction patterns. It is expected that the design of the social media functions will have an impact on the user behavior of social support exchange. In this part of a multipart study, we investigate the social support types, in particular information support types, across multiple computer-mediated communication formats (forum, journal, and notes) within an alcoholism community using descriptive content analysis on 3 months of data from a MedHelp online peer support community. We present the results of identified informational support types including advice, referral, fact, personal experiences, and opinions, either offered or requested. Fact type was exchanged most often among the messages; however, there were some different patterns between notes and journal posts. Notes were used for maintaining relationships rather than as a main source for seeking information. Notes were similar to comments made to journal posts, which may indicate the friendship between journal readers and the author. These findings suggest that users may have initially joined the MedHelp Alcoholism Community for information-seeking purposes but continue participation even after they have completed with information gathering because of the relationships they formed with community members through social media features.

0.005299047 = product of:
  0.021196188 = sum of:
    0.021196188 = product of:
      0.042392377 = sum of:
        0.042392377 = weight(_text_:processing in 5049) [ClassicSimilarity], result of:
          0.042392377 = score(doc=5049,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.22363065 = fieldWeight in 5049, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5049)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

Based on the salient features of the documents, automatic text summarization systems extract the key sentences from source documents. This process supports the users in evaluating the relevance of the extracted documents returned by information retrieval systems. Because of this tool, efficient filtering can be achieved. Indirectly, these systems help to resolve the problem of information overloading. Many automatic text summarization systems have been implemented for use with different languages. It has been established that the grammatical and lexical differences between languages have a significant effect on text processing. However, the impact of the language differences on the automatic text summarization systems has not yet been investigated. The authors provide an impact analysis of language difference on automatic text summarization. It includes the effect on the extraction processes, the scoring mechanisms, the performance, and the matching of the extracted sentences, using the parallel corpus in English and Chinese as the tested object. The analysis results provide a greater understanding of language differences and promote the future development of more advanced text summarization techniques.

0.004239238 = product of:
  0.016956951 = sum of:
    0.016956951 = product of:
      0.033913903 = sum of:
        0.033913903 = weight(_text_:processing in 1683) [ClassicSimilarity], result of:
          0.033913903 = score(doc=1683,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.17890452 = fieldWeight in 1683, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.03125 = fieldNorm(doc=1683)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

As the demand for global information increases significantly, multilingual corpora has become a valuable linguistic resource for applications to cross-lingual information retrieval and natural language processing. In order to cross the boundaries that exist between different languages, dictionaries are the most typical tools. However, the general-purpose dictionary is less sensitive in both genre and domain. It is also impractical to manually construct tailored bilingual dictionaries or sophisticated multilingual thesauri for large applications. Corpusbased approaches, which do not have the limitation of dictionaries, provide a statistical translation model with which to cross the language boundary. There are many domain-specific parallel or comparable corpora that are employed in machine translation and cross-lingual information retrieval. Most of these are corpora between Indo-European languages, such as English/French and English/Spanish. The Asian/Indo-European corpus, especially English/Chinese corpus, is relatively sparse. The objective of the present research is to construct English/ Chinese parallel corpus automatically from the World Wide Web. In this paper, an alignment method is presented which is based an dynamic programming to identify the one-to-one Chinese and English title pairs. The method includes alignment at title level, word level and character level. The longest common subsequence (LCS) is applied to find the most reliabie Chinese translation of an English word. As one word for a language may translate into two or more words repetitively in another language, the edit operation, deletion, is used to resolve redundancy. A score function is then proposed to determine the optimal title pairs. Experiments have been conducted to investigate the performance of the proposed method using the daily press release articles by the Hong Kong SAR government as the test bed. The precision of the result is 0.998 while the recall is 0.806. The release articles and speech articles, published by Hongkong & Shanghai Banking Corporation Limited, are also used to test our method, the precision is 1.00, and the recall is 0.948.

0.0039652926 = product of:
  0.01586117 = sum of:
    0.01586117 = product of:
      0.03172234 = sum of:
        0.03172234 = weight(_text_:22 in 2738) [ClassicSimilarity], result of:
          0.03172234 = score(doc=2738,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = 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)
  0.25 = coord(1/4)

Date

22. 3.2009 12:51:47

0.0027757047 = product of:
  0.011102819 = sum of:
    0.011102819 = product of:
      0.022205638 = sum of:
        0.022205638 = weight(_text_:22 in 1616) [ClassicSimilarity], result of:
          0.022205638 = score(doc=1616,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = 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)
  0.25 = coord(1/4)

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.