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1341
and Computer Application
In: http://www.ijcaonline.org/volume5/number3/pxc3871269.pdf
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1342
Everyone’s a Critic: Memory Models and Uses for an Artificial Turing Judge 1
In: http://agi-conf.org/2009/papers/paper_19.pdf
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1343
From CLEF to TrebleCLEF: promoting Technology Transfer for Multilingual Information Retrieval
In: http://dis.shef.ac.uk/mark/publications/my_papers/DELOS_CLEFtoTrebleCLEF_20071116-d.pdf
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1344
CLEF: Ongoing Activities and Plans for the Future
In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings6/NTCIR/85.pdf
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1345
Automatic Summarization based on Field Coherent Passages
In: http://research.ijcaonline.org/volume79/number9/pxc3891636.pdf
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1346
The Use of Application Domain Knowledge (and Other Techniques) for Facilitating Requirements Capture
In: http://www.soi.city.ac.uk/homes/till/wp31.ps
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1347
An Elaboration on the Effect of Reading Anxiety on Reading Achievement
In: http://www.ccsenet.org/journal/index.php/elt/article/download/50571/27176/
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1348
Language Learning Achievement among Tertiary Students
In: http://www.journals.aiac.org.au/index.php/IJALEL/article/download/1164/1217/
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1349
A “Catchy ” Copy and Concept Evaluation System Using a Natural Language Processing Approach
In: http://ultimavi.arc.net.my/banana/Workshop/SCI2002/papers/Ikada_Kaneda.pdf
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1350
Diagnostic Language Testing for Taiwanese University Students: The Online English Assessment System (OEAS) Project
In: http://fllcccu.ccu.edu.tw/conference/2005conference_2/download/C44.pdf
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1351
A Model of Foreign Language Anxiety in the Saudi EFL Context
In: http://ccsenet.org/journal/index.php/elt/article/download/37818/21138/
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1352
Team Name
In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings10/pdf/NTCIR/MedNLP/08-NTCIR10-MEDNLP-KuL.pdf
Abstract: In this paper, we describe the Sinica-Yuntech system (TeamID: SinicaNLP) at the NTCIR-10 MedNLP task. Materials of the MedNLP task are in Japanese. However, having only Chinese resources and knowledge, we need to translate these materials into Chinese. Two preprocessing approaches, different in the timing of translation, were taken. One was to translate Japanese sentences in to Chinese ones, and then to perform segmentation and part of speech tagging on these Chinese sentences; the other was to segment and tag parts of speech on Japanese sentences, and then to translate the composite words. After knowing words and their parts of speech, we identified symptoms and diseases by a vocabulary matching approach. The Internet searching results and parts of speech patterns were also utilized to recognize out of vocabulary symptoms. After recognizing the targets in Chinese, a reverse translation was performed in order to label the original Japanese materials. We merged the tags from vocabulary matching, Internet searching and pattern mapping to obtain the performance of our best run: an f-score 53.88 and an accuracy
Keyword: disease; Experimentation. Keywords Cross-lingual; medical natural language processing; part of speech pattern; Performance; symptom; Test analysis. General Terms Algorithms
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.648.102
http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings10/pdf/NTCIR/MedNLP/08-NTCIR10-MEDNLP-KuL.pdf
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1353
Language Anxiety and the Achievement of School based Oral English Test among Malaysian Form Four Students
In: http://ijlter.org/index.php/ijlter/article/viewFile/32/pdf/
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1354
The relation between vocabulary and mental age of a rural white population
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1355
On the reliability of switching costs across time and domains
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