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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 20 of 20 1 Detecting Opinions and their Opinion Targets Yoonjung Choi; Seongchan Kim; Sung-hyon Myaeng In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings8/NTCIR/07-NTCIR8-MOAT-ChoiY.pdf (2010) BASE Show details 2 Domain-specific sentiment analysis using contextual feature generation Yoonjung Choi; Youngho Kim; Sung-hyon Myaeng In: http://ir.kaist.ac.kr/papers/2009/tsa09.pdf (2009) BASE Show details 3 Opinion Analysis based on Lexical Clues and their Expansion Youngho Kim; Sung-hyon Myaeng In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings6/NTCIR/53.pdf (2007) BASE Show details 4 Opinion Analysis based on Lexical Clues and their Expansion Youngho Kim; Sung-hyon Myaeng In: http://ir.kaist.ac.kr/papers/2007/yhkim_opinion_final - NTCIR.pdf (2007) BASE Show details 5 Automatic Identification of Text Genres and Their Roles in SubjectBased Categorization Yong-bae Lee; Sung Hyon Myaeng In: http://csdl.computer.org/comp/proceedings/hicss/2004/2056/04/205640100b.pdf (2004) Abstract: Genre characterizes text differently than the usual subject or prepositional content that has been the focus of most information retrieval and classification research. We developed a new method for automatic genre classification that is based on statistically selected features obtained from both subject-classified and genre-classified training data. The main idea of the genre classification method is to calculate the weight of a feature for a genre class by using its frequency statistics for subject classifications. Having observed that the deviation formula and discrimination formula using document frequency ratios work as expected, we went on to study the roles of various types of features such as content-bearing words, function words, morphemes, and punctuations marks. In the first part of this paper, we present some of our findings in the roles of the feature types for genre classification, with a brief discussion of the genre-based classification. The genre classes we used are those often found in Web documents: accident reportages, newspaper editorials, personal homepages, product reviews, product specifications, research articles, and Q&A’s. The second part of the paper addresses the issue of how text genres help classifying documents based on the subject content of documents. This is a corollary to our original hypothesis that subject classification would help identifying the genre class of a document automatically. Our experimental work shows that while subject classes clearly help improving the genre-based classification, it is not clear whether using the genre class information for documents in the same way helps subject-based classification. However, we found that training a subject classifier with a set of documents belonging to a particular genre class improves subject-based classification. 1. URL: http://csdl.computer.org/comp/proceedings/hicss/2004/2056/04/205640100b.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.106.1021 BASE Hide details 6 Overview of clir task at the third ntcir workshop Kuang-hua Chen; Hsin-hsi Chen; Noriko K... In: http://nlg.csie.ntu.edu.tw/conference_papers/ntcir2002a.pdf (2002) BASE Show details 7 Complementing Dictionary-Based Query Translations with Corpus Statistics for Cross-Language IR Sung Hyon Myaeng; Myung-gil Jang In: http://www.mt-archive.info/MTS-1999-Myaeng.pdf (1999) BASE Show details 8 Using Mutual Information to Resolve Query Translation Ambiguities And Query; Myung-gil Jang; Sung Hyon Myaeng... In: http://acl.ldc.upenn.edu/P/P99/P99-1029.pdf (1999) BASE Show details 9 Using Syntactic Dependencies and WordNet Classes for Noun Event Recognition Yoonjae Jeong; Sung-hyon Myaeng In: http://ceur-ws.org/Vol-902/paper_5.pdf BASE Show details 10 Unsupervised word sense disambiguation using Wordnet Relatives; Hee-cheol Seo A; Hoojung Chung A... In: http://ir.icu.ac.kr/papers/Unsupervised_Word_Sense_Disambiguation.pdf BASE Show details 11 Detecting experiences from weblogs Keun Chan Park; Yoonjae Jeong; Sung Hyon Myaeng In: http://aclweb.org/anthology-new/P/P10/P10-1148.pdf BASE Show details 12 Concept Unification of Terms in Different Languages for IR Qing Li; Sung-hyon Myaeng In: http://www.mt-archive.info/Coling-ACL-2006-Li.pdf BASE Show details 13 Concept Unification of Terms in Different Languages for IR Qing Li; Sung-hyon Myaeng; Yun Jin... In: http://ir.kaist.ac.kr/papers/2006/Concept Unification of Terms in Different Languages for IR.pdf BASE Show details 14 Proceedingsof the Third NTCIR Workshop Overview of CLIR Task at the Third NTCIR Workshop Kuang-hua Chen; Hsin-hsi Chen; Noriko K... In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings3/NTCIR3-OV-CLIR-ChenK.rev.pdf BASE Show details 15 ***Department of Statistics, Chungnam National University Noriko K; Hsin-hsi Chen; Sung Hyon Myaeng... In: http://nlg.csie.ntu.edu.tw/conference_papers/ntcir2005d.pdf BASE Show details 16 Generating and Mixing Feature Sets from Language Models for Sentiment Classification Yoonjae Jeong; Youngho Kim; Sung-hyon Myaeng In: http://ir.kaist.ac.kr/papers/2009/nlp-ke2009.pdf BASE Show details 17 Automatic Discovery of Technology Trends from Patent Text Youngho Kim; Yingshi Tian; Yoonjae Jeong... In: http://ir.kaist.ac.kr/papers/2008/20081025_2009_SAC_Camera-ready.pdf BASE Show details 18 Simple Query Translation Methods for Korean-English and Korean-Chinese CLIR in NTCIR Experiments Myung Gil Jang; Jang Myung; Gil Jang... In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings3/NTCIR3-CLIR-JangM.pdf BASE Show details 19 Concept Unification of Terms in Different Languages for IR Qing Li; Sung-hyon Myaeng; Yun Jin... In: http://acl.ldc.upenn.edu/P/P06/P06-1081.pdf BASE Show details 20 Using Mutual Information to Resolve Query Translation Ambiguities and Query Term Weighting Myung-Gil Jang; Sung Hyon Myaeng; Se Young Park BASE Show details

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