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Research on Aspect-Level Sentiment Analysis Based on Text Comments

Jing Tian, Wushour Slamu, Miaomiao Xu, Chunbo Xu, Xue Wang
<span title="2022-05-23">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nzoj5rayr5hutlurimhzyjlory" style="color: black;">Symmetry</a> </i> &nbsp;
In order to symmetrically simulate the complex relationship between aspect contexts and accurately extract the polarity of emotional features, this paper combines the latest development trend of NLP, combines  ...  ACSA: Aspect Category Sentiment Analysis Dataset.  ...  Acknowledgments: We thank the anonymous reviewers for their valuable feedback. Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/sym14051072">doi:10.3390/sym14051072</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ea3lhojwzvh65hkf3c6xf3oup4">fatcat:ea3lhojwzvh65hkf3c6xf3oup4</a> </span>
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A Multi-staged Feature-Attentive Network for Fashion Clothing Classification and Attribute Prediction

Majuran Shajini, Amirthalingam Ramanan
<span title="">2022</span> <i title="Computer Vision Center Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tacfpjxccve3bmtvwxtrw3krn4" style="color: black;">ELCVIA Electronic Letters on Computer Vision and Image Analysis</a> </i> &nbsp;
In this work, we introduce a multi-staged feature-attentive network to attain clothing category classification and attribute prediction.  ...  Experimental results show that the proposed architectures for supervised and semi-supervised learning entailing deep convolutional neural network outperforms many state-of-the-art techniques considerably  ...  representation through combining low-level and high-level features of convolutional neural network.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5565/rev/elcvia.1409">doi:10.5565/rev/elcvia.1409</a> <a target="_blank" rel="external noopener" href="https://doaj.org/article/7e40330bee9440a4b649290d64a22d91">doaj:7e40330bee9440a4b649290d64a22d91</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dpyasqxfazcidha56nsuhfqsuq">fatcat:dpyasqxfazcidha56nsuhfqsuq</a> </span>
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Leveraging Contextual Sentences for Text Classification by Using a Neural Attention Model

DanFeng Yan, Shiyao Guo
<span title="2019-08-01">2019</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3wwzxqpotbc73bzpemzybzg7ee" style="color: black;">Computational Intelligence and Neuroscience</a> </i> &nbsp;
We explored several approaches to incorporate context information in the deep learning framework for text classification, including designing different attention mechanisms based on different neural network  ...  We propose two kinds of classification algorithms: one is based on convolutional neural network fusing context information and the other is based on bidirectional long and short time memory network.  ...  For now, applying deep learning to solve large-scale text classification problem is the most important thing in text representation domain. e way is to use convolutional neural network (CNN) or recurrent  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2019/8320316">doi:10.1155/2019/8320316</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31467518">pmid:31467518</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6701294/">pmcid:PMC6701294</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/x3z7gstfafactp5xcjy327qs4q">fatcat:x3z7gstfafactp5xcjy327qs4q</a> </span>
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An Integration model based on Graph Convolutional Network for Text Classification

Hengliang Tang, Yuan Mi, Fei Xue, Yang Cao
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Graph Convolutional Network (GCN) is extensively used in text classification tasks and performs well in the process of the non-euclidean structure data.  ...  INDEX TERMS Bidirectional long short-term memory network, dependency relationship, graph convolutional network, part-of-speech information, text classification.  ...  Network (BRNN) [9] , Long Short-Term Memory (LSTM) [10] Network, Gated Recurrent Unit (GRU) [11] , Recurrent Convolutional Neural Network (RCNN) [12] , Convolutional Recurrent Neural Network (CRNN  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3015770">doi:10.1109/access.2020.3015770</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2pz7g7dh3jdmdnr2qaaebzwl5e">fatcat:2pz7g7dh3jdmdnr2qaaebzwl5e</a> </span>
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Neural Ranking Models for Document Retrieval [article]

Mohamed Trabelsi, Zhiyu Chen, Brian D. Davison, Jeff Heflin
<span title="2021-02-23">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking.  ...  Recently, researchers have leveraged deep learning models in information retrieval.  ...  Abcnn: Attention-based convolutional neural network for modeling sentence pairs. Transactions of the Association for Computational Linguistics, 4, 259-272.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2102.11903v1">arXiv:2102.11903v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zc2otf456rc2hj6b6wpcaaslsa">fatcat:zc2otf456rc2hj6b6wpcaaslsa</a> </span>
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Adversarial nets with perceptual losses for text-to-image synthesis [article]

Miriam Cha, Youngjune Gwon, H. T. Kung
<span title="2017-08-30">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Recent approaches in generative adversarial networks (GANs) can automatically synthesize realistic images from descriptive text.  ...  In this paper, we aim to extend state of the art for GAN-based text-to-image synthesis by improving perceptual quality of generated images.  ...  Deep convolutional neural networks for GAN Convolutional neural nets (CNNs) remain to be state-of-theart for visual recognition tasks. Radford et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1708.09321v1">arXiv:1708.09321v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jfvpbfimwbckdovddt3gyzjrf4">fatcat:jfvpbfimwbckdovddt3gyzjrf4</a> </span>
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Feature Fusion Text Classification Model Combining CNN and BiGRU with Multi-Attention Mechanism

Jingren Zhang, Fang'ai Liu, Weizhi Xu, Hui Yu
<span title="2019-11-12">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hijy7jexkvcipg3tulqv73bck4" style="color: black;">Future Internet</a> </i> &nbsp;
Convolutional neural networks (CNN) and long short-term memory (LSTM) have gained wide recognition in the field of natural language processing.  ...  However, due to the pre- and post-dependence of natural language structure, relying solely on CNN to implement text categorization will ignore the contextual meaning of words and bidirectional long short-term  ...  Acknowledgments: Thanks to all commenters for their valuable and constructive comments. Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/fi11110237">doi:10.3390/fi11110237</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pfqmpewbbngv5ehlysxb5m32fe">fatcat:pfqmpewbbngv5ehlysxb5m32fe</a> </span>
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Cross Modal Few-Shot Contextual Transfer for Heterogenous Image Classification

Zhikui Chen, Xu Zhang, Wei Huang, Jing Gao, Suhua Zhang
<span title="2021-05-24">2021</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/el4ui6zhlfcjjbeubbsd7m4x6i" style="color: black;">Frontiers in Neurorobotics</a> </i> &nbsp;
To alleviate the difficulty, we propose a cross-modal few-shot contextual transfer method that leverages the contextual information as a supplement and learns context awareness transfer in few-shot image  ...  classification scenes, which fully utilizes the information in heterogeneous data.  ...  METHOD Classical convolutional neural network (CNN)-based image classification tasks often fail in directly using a few-shot learning scenario, since the models rely on well-trained feature extractors  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fnbot.2021.654519">doi:10.3389/fnbot.2021.654519</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34108871">pmid:34108871</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8180855/">pmcid:PMC8180855</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ue5u75pc6nf7hgpkbj6zjry4ie">fatcat:ue5u75pc6nf7hgpkbj6zjry4ie</a> </span>
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Graph Convolution Over Multiple Latent Context-Aware Graph Structures for Event Detection

Lei Li, Li Jin, Zequn Zhang, Qing Liu, Xian Sun, Hongqi Wang
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Sequence-based Neural Network Models • DMCNN [7] , which exploits a dynamic multi-pooling convolutional neural network for event trigger detection. • dbRNN [10] , which adds dependency bridges with weight  ...  We introduce the graph convolutional networks with residual connections to combine the local and the non-local contextual information, which can effectively enhance the information flow of graph structure  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3024872">doi:10.1109/access.2020.3024872</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zbz54yswpba6bjonxlggdyj2jm">fatcat:zbz54yswpba6bjonxlggdyj2jm</a> </span>
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Multimodal Emotion Recognition from Art Using Sequential Co-Attention

Tsegaye Misikir Tashu, Sakina Hajiyeva, Tomas Horvath
<span title="2021-08-21">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/au3ye363lzbdroopx7rzfyv63m" style="color: black;">Journal of Imaging</a> </i> &nbsp;
The proposed architecture helps the model to focus on learning informative and refined representations for both feature extraction and modality fusion.  ...  In this study, we present a multimodal emotion recognition architecture that uses both feature-level attention (sequential co-attention) and modality attention (weighted modality fusion) to classify emotion  ...  A Convolutional Neural Network (CNN) and a Bi-directional Gated Recurrent Unit (Bi-GRU) is used to obtain the text vectors.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/jimaging7080157">doi:10.3390/jimaging7080157</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34460793">pmid:34460793</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8404915/">pmcid:PMC8404915</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xktlzcr2zbc4rdb6tlk6uhh46i">fatcat:xktlzcr2zbc4rdb6tlk6uhh46i</a> </span>
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A Densely Connected GRU Neural Network Based on Coattention Mechanism for Chinese Rice-Related Question Similarity Matching

Haoriqin Wang, Huaji Zhu, Huarui Wu, Xiaomin Wang, Xiao Han, Tongyu Xu
<span title="2021-06-27">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/yws6xlpwzjf7llvhkn3wayuvfu" style="color: black;">Agronomy</a> </i> &nbsp;
Combined with the agricultural word segmentation dictionary, we applied Word2vec with the TF-IDF method, effectively solving the problem of high dimension and sparse data in the rice-related text.  ...  Each network layer employed the connection information of features and all previous recursive layers' hidden features.  ...  [13] introduced CNN (Convolutional Neural Networks) networks into the DSSM model to retain more contextual information.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/agronomy11071307">doi:10.3390/agronomy11071307</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qag5pszb5fh35m5whxvimlrjhu">fatcat:qag5pszb5fh35m5whxvimlrjhu</a> </span>
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Directionally Constrained Fully Convolutional Neural Network For Airborne Lidar Point Cloud Classification [article]

Congcong Wen, Lina Yang, Ling Peng, Xiang Li, Tianhe Chi
<span title="2019-08-19">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To make full use of the orientation information of neighborhood points, the proposed D-Conv module performs convolution in an orientation-aware manner by using a directionally constrained nearest neighborhood  ...  In this paper, we proposed a directionally constrained fully convolutional neural network (D-FCN) that can take the original 3D coordinates and LiDAR intensity as input; thus, it can directly apply to  ...  ACKNOWLEDGEMENTS (OPTIONAL) We thank the NYU MMVC Lab for providing GPU-equipped servers for these experiments.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1908.06673v1">arXiv:1908.06673v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/smgwykeubbfebkym47fhxeapva">fatcat:smgwykeubbfebkym47fhxeapva</a> </span>
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Music Recommendation System and Recommendation Model Based on Convolutional Neural Network

Yezi Zhang, Chia-Huei Wu
<span title="2022-05-12">2022</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/khqygtfojnby3jyh5obuwd7ko4" style="color: black;">Mobile Information Systems</a> </i> &nbsp;
Two recommended methods based on convolutional neural networks are tested in this article.  ...  In this paper, we take digital piano music as the research object, form comprehensive features using spectrum and notes, design classification methods using convolutional neural networks, and further process  ...  Convolutional neural network combines the advantages of image processing and deep learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2022/3387598">doi:10.1155/2022/3387598</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/asa4bt6j2naxfnuxihkhdp6hdq">fatcat:asa4bt6j2naxfnuxihkhdp6hdq</a> </span>
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Deep Learning Based Text Classification: A Comprehensive Review [article]

Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, Jianfeng Gao
<span title="2021-01-04">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we provide a comprehensive review of more than 150 deep learning based models for text classification developed in recent years, and discuss their technical contributions, similarities,  ...  We also provide a summary of more than 40 popular datasets widely used for text classification.  ...  ACKNOWLEDGMENTS The authors would like to thank Richard Socher, Kristina Toutanova, and Brooke Cowan for reviewing this work, and providing very insightful comments.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.03705v3">arXiv:2004.03705v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/al5hstylsbhfpldvokuvlpomam">fatcat:al5hstylsbhfpldvokuvlpomam</a> </span>
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RepGN:Object Detection with Relational Proposal Graph Network [article]

Xingjian Du, Xuan Shi, Risheng Huang
<span title="2019-04-18">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we explore the idea of modeling the relationships among the proposals for object detection from the graph learning perspective.  ...  Besides, we propose a novel graph-cut based pooling layer for hierarchical coarsening of the graph, which empowers the RepGN module to exploit the inter-regional correlation and scene description in a  ...  [18] developed feature extractor by packing the recurrent neural networks into the convolutional neural networks, with which it can combine global and local information in object detection.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.08959v1">arXiv:1904.08959v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bjhpmg5qzvdpvknwdpmgad3lci">fatcat:bjhpmg5qzvdpvknwdpmgad3lci</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200828062847/https://arxiv.org/pdf/1904.08959v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f3/ea/f3ea2d4e72ee64980eb64273098b3ec88272c4ab.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.08959v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>
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