Fuji Ren


2014

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Gene–disease association extraction by text mining and network analysis
Changqin Quan | Fuji Ren
Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi)

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Real Time Early-stage Influenza Detection with Emotion Factors from Sina Microblog
Xiao Sun | Jiaqi Ye | Fuji Ren
Proceedings of the Fifth Workshop on South and Southeast Asian Natural Language Processing

2013

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Proceedings of the Seventh SIGHAN Workshop on Chinese Language Processing
Liang-Chih Yu | Yuen-Hsien Tseng | Jingbo Zhu | Fuji Ren
Proceedings of the Seventh SIGHAN Workshop on Chinese Language Processing

2012

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Emotion Estimation from Sentence Using Relation between Japanese Slangs and Emotion Expressions
Kazuyuki Matsumoto | Kenji Kita | Fuji Ren
Proceedings of the 26th Pacific Asia Conference on Language, Information, and Computation

2011

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Exploring Emotional Words for Chinese Document Chief Emotion Analysis
Yunong Wu | Kenji Kita | Fuji Ren | Kazuyuki Matsumoto | Xin Kang
Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation

2010

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An Exploration of Features for Recognizing Word Emotion
Changqin Quan | Fuji Ren
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Automatic Annotation of Word Emotion in Sentences Based on Ren-CECps
Changqin Quan | Fuji Ren
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Textual information is an important communication medium contained rich expression of emotion, and emotion recognition on text has wide applications. Word emotion analysis is fundamental in the problem of textual emotion recognition. Through an analysis of the characteristics of word emotion expression, we use word emotion vector to describe the combined basic emotions in a word, which can be used to distinguish direct and indirect emotion words, express emotion ambiguity in words, and express multiple emotions in words. Based on Ren-CECps (a Chinese emotion corpus), we do an experiment to explore the role of emotion word for sentence emotion recognition and we find that the emotions of a simple sentence (sentence without negative words, conjunctions, or question mark) can be approximated by an addition of the word emotions. Then MaxEnt modeling is used to find which context features are effective for recognizing word emotion in sentences. The features of word, N-words, POS, Pre-N-words emotion, Pre-is-degree-word, Pre-is-negativeword, Pre-is-conjunction and their combination have been experimented. After that, we use the two metrics: Kappa coefficient of agreement and Voting agreement to measure the word annotation agreement of Ren-CECps. The experiments on above context features showed promising results compared with word emotion agreement on people's judgments.

2009

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Construction of a Blog Emotion Corpus for Chinese Emotional Expression Analysis
Changqin Quan | Fuji Ren
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Accurate Learning for Chinese Function Tags from Minimal Features
Caixia Yuan | Fuji Ren | Xiaojie Wang
Proceedings of the ACL-IJCNLP 2009 Student Research Workshop

2007

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Speaker Identification Method Using Earth Mover’s Distance for CCC Speaker Recognition Evaluation 2006
Shingo Kuroiwa | Satoru Tsuge | Masahiko Kita | Fuji Ren
International Journal of Computational Linguistics & Chinese Language Processing, Volume 12, Number 3, September 2007: Special Issue on Invited Papers from ISCSLP 2006

2006

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Machine Transliteration
Mohamed Abdel Fattah | Fuji Ren | Shingo Kuroiwa
Proceedings of the 20th Pacific Asia Conference on Language, Information and Computation

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A Chinese Automatic Text Summarization system for mobile devices
Lei Yu | Mengge Liu | Fuji Ren | Shingo Kuroiwa
Proceedings of the 20th Pacific Asia Conference on Language, Information and Computation

2002

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Annotating the functional chunks in Chinese sentences
Qiang Zhou | Elliott Franco Drabek | Fuji Ren
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)