A model of persuasion design PD. B&L = business and law
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I am now a Ph.D. student in the Social Media Analysis Lab, Department of Computer Science and Technology at Fudan University. Follow. Ruotian Ma 1, Minlong Peng , Qi Zhang 1;3, Zhongyu Wei2, Xuanjing Huang 1Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University 2School of Data Science, Fudan University 3Research Institute of Intelligent and Complex Systems, Fudan University frtma19,mlpeng16,qz,zywei,xjhuangg@fudan.edu.cn Minlong Peng, Ruotian Ma, Qi Zhang, Lujun Zhao, Mengxi Wei, Changlong Sun and Xuanjing Huang. From Disjoint Sets to Parallel Data to Train Seq2Seq Models for Sentiment Transfer. Paulo Cavalin, Marisa Vasconcelos, Marcelo Grave, Claudio Pinhanez and Victor Henrique Alves Ribeiro.
However, Lattice-LSTM suffers from a complicated model architecture, resulting in low computational Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning Minlong Peng, Xiaoyu Xing , Qi Zhang, Jinlan Fu, Xuanjing Huang 2019-09-18 · Authors: Minlong Peng, Qi Zhang, Xuanjing Huang (Submitted on 18 Sep 2019) Abstract: Cross-domain sentiment analysis is currently a hot topic in the research and engineering areas. Ruotian Ma, Minlong Peng, Qi Zhang, Zhongyu Wei, Xuanjing Huang, A Unified MRC Framework for Named Entity Recognition Xiaoya Li, Jingrong Feng, Yuxian Meng, Qinghong Han, Fei Wu, Jiwei Li, Minlong Peng, Qi Zhang, Yu-gang Jiang, Xuanjing Huang The task of adopting a model with good performance to a target domain that is different from the source domain used for training has received considerable attention in sentiment analysis. 2021-04-06 · Minlong Peng, Xiaoyu Xing, Qi Zhang, Jinlan Fu, Xuanjing Huang. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.
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From Disjoint Sets to Parallel Data to Train Seq2Seq Models for Sentiment Transfer. Paulo Cavalin, Marisa Vasconcelos, Marcelo Grave, Claudio Pinhanez and Victor Henrique Alves Ribeiro. Document Ranking with a Pretrained Sequence-to-Sequence Model.
A model of persuasion design PD. B&L = business and law
2019-10-18 · Minlong Peng. 1; Xuanjing Huang. 1; 1.
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Minlong Peng. Fudan University. Verified email at fudan.edu.cn - Homepage. Machine Learning Natural Language Processing. M Peng, R Ma, Q Zhang, L Zhao, M Wei, C
Minlong Peng Qi Zhang \textit{Multiple Instance Learning} (MIL) is concerned with learning from bags of instances, where only bag labels are given and instance labels are unknown.
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Social Media | Mention Recommendation | Reinforcement Learning. Multi-view Embedding-based Synonyms for Email Search. Cheng Li, Mingyang Zhang, Michael Tao Gui, Yicheng Zou, Qi Zhang, Minlong Peng, Jinlan Fu, Zhongyu Wei and Xuanjing Huang; A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers Aditi Chaudhary, Jiateng Xie, Zaid Sheikh, Graham Neubig and Jaime Carbonell; A Logic-Driven Framework for Consistency of Neural Models Trainable Undersampling for Class-Imbalance Learning Minlong Peng1, Qi Zhang1, Xiaoyu Xing1, Tao Gui1, Xuanjing Huang1 Yu-Gang Jiang1, Keyu Ding2, Zhigang Chen2 School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, 2019-08-14 Minlong Peng, Qi Zhang, Xiaoyu Xing, Tao Gui, Jinlan Fu and Xuanjing Huang School of Computer Science, Fudan University, Shanghai, China fmlpeng16, qz, xyxing18, tgui16, fujl16, xjhuangg@fudan.edu.cn Abstract Word representation is a key component in neural-network-based sequence labeling systems.
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∙ FUDAN University ∙ 0 ∙ share In this work, we explore the way to perform named entity recognition (NER) using only unlabeled data and named entity dictionaries. Tao Gui, Peng Liu, Qi Zhang, Liang Zhu, Minlong Peng, Yunhua Zhou and Xuanjing Huang Social Media | Mention Recommendation | Reinforcement Learning Multi-view Embedding-based Synonyms for Email Search Peng, Minlong (Fudan university) Peng, Wen-Chih (National Chiao Tung University) Pereira Nunes, Bernardo (Australian National University) Perera, Sujan (IBM Watson) Pesquita, Catia (LaSIGE, Faculdade de Ciências, Universidade de Lisboa) Pibiri, Giulio Ermanno (University of Pisa) Piccardi, Tiziano (Ecole Polytechnique Fédérale de Lausanne) 上领英，在全球领先职业社交平台查看minlong peng的职业档案。minlong的职业档案列出了教育经历。上领英，查看minlong的完整档案，结识职场人脉和查看相似公司的职位。 Reject complicated operations for incorporating lexicon for Chinese NER. - v-mipeng/LexiconAugmentedNER Minlong Peng, Qi Zhang, Yu-gang Jiang, Xuanjing Huang Shanghai Key Laboratory of Intelligent Information Processing, Fudan University School of Computer Science, Fudan University 825 Zhangheng Road, Shanghai, China fmlpeng16,qz,ygj,xjhuangg@fudan.edu.cn Abstract The task of adopting a model with good performance to a target domain that is Minlong Peng, Qi Zhang, Yu-gang Jiang, Xuanjing Huang. Incorporating Latent Meanings of Morphological Compositions to Enhance Word Embeddings. Yang Xu, Jiawei Liu, Wei Yang, Liusheng Huang. Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game. Haichao Zhang, Haonan Yu, Wei Xu Tao Gui, Yicheng Zou, Qi Zhang, Minlong Peng, Jinlan Fu, Zhongyu Wei and Xuanjing Huang; A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers Aditi Chaudhary, Jiateng Xie, Zaid Sheikh, Graham Neubig and Jaime Carbonell; A Logic-Driven Framework for Consistency of Neural Models The goal of this work is to provide a keyword-suggestion-like hashtag recommendation service, which recommends several hashtags when the user types in the hashtag symbol “#” while writing a post. Different from previously published hashtag recommendation systems, which only considered the textual information of the post itself or a few numbers of the latest posts, this work proposed to Upload an image to customize your repository’s social media preview.