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Fusing Syntactic Structure Information and Lexical Semantic Information for End-to-End Aspect-Based Sentiment Analysis被引量：3
《Tsinghua Science and Technology》2023年第2期230-243,共14页Yong Bie Yan Yang Yiling Zhang 
This work was supported by the National Natural Science Foundation of China(No.61976247).
The aspect-based sentiment analysis(ABSA)consists of two subtasksaspect term extraction and aspect sentiment prediction.Most methods conduct the ABSA task by handling the subtasks in a pipeline manner,whereby problems...
关键词：deep learning natural language processing aspect-based sentiment analysis graph convolutional 
Fusing Geometrical and Visual Information via Superpoints for the Semantic Segmentation of 3D Road Scenes被引量：8
《Tsinghua Science and Technology》2020年第4期498-507,共10页Liuyuan Deng Ming Yang Zhidong Liang Yuesheng He Chunxiang Wang 
supported by the National Natural Science Foundation of China(No.U1764264/61873165);Shanghai Automotive Industry Science and Technology Development Foundation(No.1807);the International Chair on Automated Driving of Ground Vehicle.
This paper addresses the problem of the semantic segmentation of large-scale 3D road scenes by incorporating the complementary advantages of point clouds and images.To make full use of geometrical and visual informati...
关键词：SCENE understanding point cloud SEMANTIC segmentation MULTI-MODAL information fusion deep learning 
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