R
Ruifeng Xu
Researcher at Harbin Institute of Technology
Publications - 191
Citations - 5117
Ruifeng Xu is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Sentiment analysis & Context (language use). The author has an hindex of 30, co-authored 190 publications receiving 3509 citations. Previous affiliations of Ruifeng Xu include Sun Yat-sen University & City University of Hong Kong.
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Journal ArticleDOI
Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN
TL;DR: A divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type, which shows that sentence type classification can improve the performance of sentence-level sentiment analysis.
Journal ArticleDOI
iDNA-Prot|dis: Identifying DNA-Binding Proteins by Incorporating Amino Acid Distance-Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition
TL;DR: It is anticipated that the iDNA-Prot|dis predictor may become a useful high throughput tool for large-scale analysis of DNA-binding proteins, or at the very least, play a complementary role to the existing predictors in this regard.
Journal ArticleDOI
Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection
Bin Liu,Deyuan Zhang,Ruifeng Xu,Jinghao Xu,Xiaolong Wang,Qingcai Chen,Qiwen Dong,Kuo-Chen Chou +7 more
TL;DR: This work proposes a novel approach, the so-called profile-based protein representation, to extract the evolutionary information via the frequency profiles, which can be calculated from the multiple sequence alignments generated by PSI-BLAST.
Proceedings ArticleDOI
A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis
TL;DR: A new large-scale Multi-Aspect Multi-Sentiment (MAMS) dataset, in which each sentence contains at least two different aspects with different sentiment polarities, and proposes simple yet effective CapsNet and CapsNet-BERT models which combine the strengths of recent NLP advances.
Proceedings ArticleDOI
Event-driven emotion cause extraction with corpus construction
TL;DR: This paper presents a new event-driven emotion cause extraction method using multi-kernel SVMs where a syntactical tree based approach is used to represent events in text and proposes a 7-tuple definition to describe emotion cause events.