By K. Saruladha, E. Thirumagal, J. Arthi, G. Aghila (auth.), Shalini R. Urs, Jin-Cheon Na, George Buchanan (eds.)
ISBN-10: 3319035983
ISBN-13: 9783319035987
ISBN-10: 3319035991
ISBN-13: 9783319035994
This ebook constitutes the refereed complaints of the fifteenth foreign convention on Asia-Pacific electronic Libraries, ICADL 2013, held in Bangalore, India, in December 2013. The 15 revised complete papers, 6 revised brief papers and 10 poster papers have been rigorously reviewed and chosen from 87 submissions. The papers are prepared in topical sections on details retrieval; social structure for electronic libraries and data coverage; electronic library functions and platforms; info mining for electronic libraries; collaboration and groups; analysing social media and social networks; cellular units and prone; and metadata and data extraction.
Read Online or Download Digital Libraries: Social Media and Community Networks: 15th International Conference on Asia-Pacific Digital Libraries, ICADL 2013, Bangalore, India, December 9-11, 2013. Proceedings PDF
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Extra info for Digital Libraries: Social Media and Community Networks: 15th International Conference on Asia-Pacific Digital Libraries, ICADL 2013, Bangalore, India, December 9-11, 2013. Proceedings
Sample text
4b shows the evaluation results for using the documents’ top-k terms. We analyzed the distribution of assigned terms for the documents in our collection. Around 10% of the documents in our collection have more than 75 terms assigned. Therefore, we computed the MAP for up to the top-75 terms. Please note, we always used all documents and only limited the number of assigned terms. As in the confidence threshold experiment the best results are achieved if all terms are considered as equally important.
For example, the term beTTagaLu is segmented as ba+e+TTa+ga+La+u. The Unicode encoding supports this segmentation. The number of these segments is the length of the term. Hence, the length of the term beTTagaLu is 6. Now on, whenever we mention length in the paper, we refer to this segment length. Next, for each term we truncate these segments iteratively one by one from the back. Truncation terminates when the term reduces by a truncation factor (α) or the term length reduces below the termination threshold (tt ).
5 for tf-idf. 8 documents. The recall of TrieIR was considerably higher than that of tf-idf and WordPress. The evaluations showed that, TrieIR performed considerably better than the TF-IDF and WordPress searches. The details of the complete evaluation are available in the technical report [1]. 3 Conclusion and Future Work This work gives new set of indexing and retrieval methods for Kannada Unicode text. Understanding of the Kannada morphologies and the Unicode encoding of Kannada text inspired the trie based indexing.
Digital Libraries: Social Media and Community Networks: 15th International Conference on Asia-Pacific Digital Libraries, ICADL 2013, Bangalore, India, December 9-11, 2013. Proceedings by K. Saruladha, E. Thirumagal, J. Arthi, G. Aghila (auth.), Shalini R. Urs, Jin-Cheon Na, George Buchanan (eds.)
by Jason
4.5