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Traditional Chinese, and Simplified Chinese. Using
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In: http://nlg.csie.ntu.edu.tw/conference_papers/nntcir2008b.pdf (2008)
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Opinion analysis across languages: An overview of and observations from the ntcir6 opinion analysis pilot task
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In: http://nlg.csie.ntu.edu.tw/conference_papers/wilf2007.pdf (2007)
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Certainty categorization model
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In: http://www.cnlp.org/publications/04RubinKandoLiddy2004_Certainty.pdf (2004)
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Abstract:
We present a theoretical framework and preliminary results for manual categorization of explicit certainty information in 32 English newspaper articles. The explicit certainty markers were identified and categorized according to the four hypothesized dimensions – perspective, focus, timeline, and level of certainty. One hundred twenty one sentences from sample news stories contained a significantly lower frequency of markers per sentence (M=0.46, SD =0.04) than 564 sentences from sample editorials (M=0.6, SD =0.23), p = 0.0056, two-tailed heteroscedastic t-test. Within each dimension, editorials had most numerous markers per sentence in high level of certainty, writer’s point of view, and future and present timeline (0.33, 0.43, 0.24, and 0.22, respectively); news stories – in high and moderate levels, directly involved third party’s point of view, and past timeline (0.19, 0.20, 0.24, and 0.20, respectively). These patterns have practical implications for automation. Further analysis of editorials showed that out of 72 combinations possible under the hypothesized model, the high level of certainty from writer’s perspective expressed abstractly in the present and future time, and expressed factually in the future were very common. Twenty two combinations never occurred; and 35 had ≤ 8 occurrences. This narrows the focus for future linguistic analysis of explicit certainty markers.
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.81.8509
http://www.cnlp.org/publications/04RubinKandoLiddy2004_Certainty.pdf
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Overview of clir task at the third ntcir workshop
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In: http://nlg.csie.ntu.edu.tw/conference_papers/ntcir2002a.pdf (2002)
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1. NTCIR Cross-Language Retrieval
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In: http://www.lrec-conf.org/proceedings/lrec2008/pdf/363_paper.pdf
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1. NTCIR Cross-Language Retrieval
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In: http://www.mt-archive.info/LREC-2008-Gey.pdf
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***Department of Statistics, Chungnam National University
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In: http://nlg3.csie.ntu.edu.tw/conference_papers/ntcir6clir.pdf
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M.: CRES at LogCLEF 2010: Towards understanding the user behaviors through an analysis of search sessions, search units and click ranks
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In: http://www.clef-initiative.eu/documents/71612/86374/CLEF2010wn-LogCLEF-TakakuEt2010.pdf
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***Department of Statistics, Chungnam National University
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In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings6/NTCIR/79.pdf
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NTCIR-GeoTime Overview: Evaluating Geographic and Temporal Search
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In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings8/NTCIR/01-NTCIR8-OV-GeoTime-GeyF.pdf
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Proceedingsof the Third NTCIR Workshop Overview of CLIR Task at the Third NTCIR Workshop
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In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings3/NTCIR3-OV-CLIR-ChenK.rev.pdf
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***Department of Statistics, Chungnam National University
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In: http://nlg.csie.ntu.edu.tw/conference_papers/ntcir2005d.pdf
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Analysis of multi-document viewpoint summarization using multi-dimensional genres
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In: http://aaaipress.org/Papers/Symposia/Spring/2004/SS-04-07/SS04-07-026.pdf
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