F
Fuji Ren
Researcher at University of Tokushima
Publications - 622
Citations - 6519
Fuji Ren is an academic researcher from University of Tokushima. The author has contributed to research in topics: Sentence & Machine translation. The author has an hindex of 30, co-authored 579 publications receiving 4966 citations. Previous affiliations of Fuji Ren include Hiroshima City University & Beijing University of Posts and Telecommunications.
Papers
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Proceedings ArticleDOI
Researches on the emotion measurement system
TL;DR: The algorithm of the emotion measurement and the prototype system based on this algorithm are proposed and the validity of this algorithm is discussed.
Journal Article
A question answering system on special domain and the implementation of speech interface
TL;DR: In this paper, a QA system was proposed to synthesize the answers retrieval from the frequent asked questions database and documents database, based on a special domain about sightseeing information, using an acoustic model HMM, a pronunciation lexicon, and a language model FSN.
Proceedings ArticleDOI
Chinese microblog sentiment classification based on convolution neural network with content extension method
TL;DR: The experiment results demonstrate that, with proper structure and parameter, the performance of the proposed deep learning method on sentiment classification is better than state-of-the-art surface learning models such as SVM or NB, which proves that DBN is suitable for short-length document classification with the proposed feature dimensionality extension method.
Journal ArticleDOI
Voting-Based Ensemble Classifiers to Detect Hedges and Their Scopes in Biomedical Texts
TL;DR: This paper applies the voting technique for the CoNLL-2010 shared task on detecting hedge cues and their scope in biomedical texts through three different voting schemes and demonstrates the effectiveness of classifiers ensemble approaches.
Proceedings ArticleDOI
Combine sentiment lexicon and dependency parsing for sentiment classification
TL;DR: A method, which combines sentiment lexicon and dependency parsing to determine the sentiment orientation and the positive or negative attitudes of the topic is proposed.