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1
Detecting Opinions and their Opinion Targets
In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings8/NTCIR/07-NTCIR8-MOAT-ChoiY.pdf (2010)
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2
Domain-specific sentiment analysis using contextual feature generation
In: http://ir.kaist.ac.kr/papers/2009/tsa09.pdf (2009)
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3
Opinion Analysis based on Lexical Clues and their Expansion
In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings6/NTCIR/53.pdf (2007)
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4
Opinion Analysis based on Lexical Clues and their Expansion
In: http://ir.kaist.ac.kr/papers/2007/yhkim_opinion_final - NTCIR.pdf (2007)
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5
Automatic Identification of Text Genres and Their Roles in SubjectBased Categorization
In: http://csdl.computer.org/comp/proceedings/hicss/2004/2056/04/205640100b.pdf (2004)
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6
Overview of clir task at the third ntcir workshop
In: http://nlg.csie.ntu.edu.tw/conference_papers/ntcir2002a.pdf (2002)
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7
Complementing Dictionary-Based Query Translations with Corpus Statistics for Cross-Language IR
In: http://www.mt-archive.info/MTS-1999-Myaeng.pdf (1999)
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8
Using Mutual Information to Resolve Query Translation
In: http://acl.ldc.upenn.edu/P/P99/P99-1029.pdf (1999)
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9
Using Syntactic Dependencies and WordNet Classes for Noun Event Recognition
In: http://ceur-ws.org/Vol-902/paper_5.pdf
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10
Unsupervised word sense disambiguation using
In: http://ir.icu.ac.kr/papers/Unsupervised_Word_Sense_Disambiguation.pdf
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11
Detecting experiences from weblogs
In: http://aclweb.org/anthology-new/P/P10/P10-1148.pdf
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12
Concept Unification of Terms in Different Languages for IR
In: http://www.mt-archive.info/Coling-ACL-2006-Li.pdf
Abstract: Due to the historical and cultural reasons, English phases, especially the proper nouns and new words, frequently appear in Web pages written primarily in Asian languages such as Chinese and Korean. Although these English terms and their equivalences in the Asian languages refer to the same concept, they are erroneously treated as independent index units in traditional Information Retrieval (IR). This paper describes the degree to which the problem arises in IR and suggests a novel technique to solve it. Our method firstly extracts an English phrase from Asian language Web pages, and then unifies the extracted phrase and its equivalence(s) in the language as one index unit. Experimental results show that the high precision of our conceptual unification approach greatly improves the IR performance. 1
URL: http://www.mt-archive.info/Coling-ACL-2006-Li.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.105.1356
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13
Concept Unification of Terms in Different Languages for IR
In: http://ir.kaist.ac.kr/papers/2006/Concept Unification of Terms in Different Languages for IR.pdf
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14
Proceedingsof the Third NTCIR Workshop Overview of CLIR Task at the Third NTCIR Workshop
In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings3/NTCIR3-OV-CLIR-ChenK.rev.pdf
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15
***Department of Statistics, Chungnam National University
In: http://nlg.csie.ntu.edu.tw/conference_papers/ntcir2005d.pdf
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16
Generating and Mixing Feature Sets from Language Models for Sentiment Classification
In: http://ir.kaist.ac.kr/papers/2009/nlp-ke2009.pdf
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17
Automatic Discovery of Technology Trends from Patent Text
In: http://ir.kaist.ac.kr/papers/2008/20081025_2009_SAC_Camera-ready.pdf
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18
Simple Query Translation Methods for Korean-English and Korean-Chinese CLIR in NTCIR Experiments
In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings3/NTCIR3-CLIR-JangM.pdf
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19
Concept Unification of Terms in Different Languages for IR
In: http://acl.ldc.upenn.edu/P/P06/P06-1081.pdf
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20
Using Mutual Information to Resolve Query Translation Ambiguities and Query Term Weighting
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