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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)
Abstract: An easy way of translating queries in one language to the other for cross-language information retrieval (IR) is to use a simple bilingual dictionary. Because of the generalpurpose nature of such dictionaries, however, this simple method yields a severe translation ambiguity problem. This paper describes the degree to which this problem arises in Korean-English cross-language IR and suggests a relatively simple yet effective method for disambiguation using mutual information statistics obtained only from the target document collection. In this method, mutual information is used not only to select the best candidate but also to assign a weight to query terms in the target language. Our experimental results based on the TREC-6 collection shows that this method can achieve up to 85% of the monolingual retrieval case and 96% of the manual disambiguation case.
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.9398
http://acl.ldc.upenn.edu/P/P99/P99-1029.pdf
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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
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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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