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Sunwei

添加825字节, 2019年5月26日 (日) 23:34

无编辑摘要

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M.Sc. Student @ IIP Group Department of Computer Science and Technology Nanjing University

Email: weisun_@outlook.com

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'''Supervisor'''

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*Professor Jun-Yuan Xie

'''Biography'''

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*I received my B.Sc. degree in of Soochow University in June 2017. In the same year, I was admitted to study for a Master degree in Nanjing University without entrance examination.  Currently I am a second year M.Sc. student of Department of Computer Science and Technology in Nanjing University and a member of IIP Group, led by professor Jun-Yuan Xie and  Chong-Jun Wang.

'''Research Interest'''

'''Machine Learning & Multi-label Learning'''.

*On '''Multi-label Text Classification (MLTC),''' text features can be regarded as '''detailed description of documents''' and label sets can be '''a summarization of documents'''. '''Hybrid Topics''' from text features and label sets by LDA (a method of '''topic model''') can effectively mine global label correlations and deeper features. Meanwhile, a pair including topics and labels can mitigate the imbalanced problem of labels. *Deep learning For multi-label text classification. We utilize '''dilated convolution''' to obtain the '''semantic understanding''' of the text and design a hybrid '''attention mechansim''' for '''different labels''' (Specifically, each label should attend to most relevant textual contents).  Firstly, we initialize trainable label embeddings. Then After obationing word-level information based on Bi-LSTM, we get semantic understanding of texts based on word-level information by dilated convolution. Finally, we design a hybrid attention for different labels based on label embeddings.  Besides, we add '''label cooccurrence matrix into loss function '''to guide the whole network to learn and achieve good results.   *'''GCN (Graph Convolution Network) '''can be used to exploit more complex label correlations on Image Multi-label Learning.

~~~~'''Publications'''</~~span~~font></~~font~~span>

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*Ran X., Pan Y., Sun W. and Wang C.. Learn to Select via Hierarchical Gate Mechanism for Aspect-Based Sentiment Analysis. In ''Proceedings of IJCAL 2019.'' *Sun W., Wang C..Multi-label Classification: Select Distinct Semantic Understanding for Different Labels[C]. In ''Proceedings of ApWeb-WAIM 2019.'' *Sun W., Ran X.. Luo X., and Wang C..An Efficient Framework by Topic Model for Multi-label Text Classification. In ''Proceedings of IJCNN 2019.'' *Xu Y., Ran X.. Sun W., Luo X. and Wang C..Gated Neural Network with Regularized Loss for Multi-label Text Classification. In ''Proceedings of IJCNN 2019.'' *Ran X., Pan Y., Sun W. and Wang C.. Modeling More Globally: A Hierarchical Attention Network via Multi-Task Learning for Aspect-Based Sentiment Analysis. In ''Proceedings of DASFAA 2019'', Chiang Mai, Thailand, Apr. 22-25, 2019: 505-509.

'''Resources'''

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*[http://manikvarma.org/downloads/XC/XMLRepository.html Extreme Classiﬁcation Repository]: for large-scale multi-label datasets and off-the-shelf eXtreme Multi-Label Learning (XML) solvers. *[http://mulan.sourceforge.net/datasets-mlc.html Mulan Multi-Label Learning Datasets]: regular/traditional multi-label learning datasets. *[https://github.com/XSilverBullet/Multi-label-Paper Related Work]: This page categorizes a list of works of my interest, mainly in Multi-Label Learning.

'''Rewards or Honors'''

*Second-Class Academic Scholarship, 2018-2019 *First-Class Academic Scholarship, 2017-2018 *Outstanding Graduate Student, 2017.06 *CCF Excellent University Student, 2016.10 *National Scholarship, 2015.11

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Sunwei

南京大学IIP