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Hashtag Recommendation Using LSTM Networks with Self-Attention

Computers, Materials & Continua 2019年第9期61卷 1261-1269页

作者： Yatian Shen Yan Li Jun Sun Wenke Ding Xianjin Shi Lei Zhang Xiajiong Shen Jing He School of Computer and Information Engineering Henan UniversityKaifeng475000China The Corporate and Investment Bank TechnologyCanary WharfLondonE145JPUK

On Twitter,people often use hashtags to mark the subject of a *** have specific themes or content that are easy for people to *** the increase in the number of tweets,how to automatically recommend hashtags for tweets has received wide *** previous hashtag recommendation methods were to convert the task into a multi-class classification ***,these methods can only recommend hashtags that appeared in historical information,and cannot recommend the new *** this work,we extend the self-attention mechanism to turn the hashtag recommendation task into a sequence labeling *** train and evaluate the proposed method,we used the real tweet data which is collected from *** results show that the proposed method can be significantly better than the most advanced *** with the state-of-the-art methods,the accuracy of our method has been increased 4%.

关键词： Hashtags recommendation self-attention neural networks sequence labeling

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Densely-connected neural networks for aspect term extraction

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Science China(Information Sciences) 2022年第6期65卷 264-266页

作者： Chen CHEN Houfeng WANG Qingqing ZHU Junfei LIU School of Software and Microelectronics Peking University MOE Key Lab of Computational Linguistics School of Electronics Engineering and Computer SciencePeking University National Engineering Research Center for Software Engineering (Peking University)

Dear editor,Aspect term extraction(ATE) is a sub-task of aspect-based sentiment analysis, which aims to extract opinionated aspect terms from user reviews. For example, in a laptop domain review: “Boot time is super ... 详细信息

关键词： aspect term extraction recurrent neural network self-attention densely-connected network double word embeddings Aspect-Based Sentiment Analysis deep learning sequence labeling

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Segmentation-Free Recognition Algorithm Based on Deep Learning for Handwritten Text Image

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Journal of Artificial Intelligence and Technology 2024年第2期4卷 169-178页

作者： Ge Peng School of Big Data Baoshan UniversityBaoshanYunnan 678000China

Segmentation-based offline handwritten character recognition algorithms suffered from the segmenting difficulty of interleaving and touching in handwritten *** tackle the problem,a segmentation-free recognition algorithm based on deep learning network is proposed in this *** network consists of four neural layers,including input layer for image preprocessing,convolutional neural networks(CNNs)layer for feature extraction,bidirectional long-short term network(BDLSTM)layer for sequence prediction,and connectionist temporal classification(CTC)layer for text sequence alignment and ***,a novel data processing method is performed for data length *** on this,groups of experiments,based on six typical databases,involved in evaluation indicators of character correct rate,training time cost,storage space cost,and testing time cost are carried *** experimental results show that the proposed algorithm has better performances in accuracy and efficiency than other classical algorithms.

关键词： deep learning image processing segmentation-free handwritten image recognition sequence labeling

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