Search Results - "Cai, Qianhua"

‘What’ and ‘Where’ both matter: dual cross-modal graph convolutional networks for multimodal named entity recognition by Zhang, Zhengxuan, Chen, Jianying, Liu, Xuejie, Mai, Weixing, Cai, Qianhua

“…Multimodal named entity recognition (MNER) aims to use visual information to assist in extracting and classifying entities in the textual content. However, it…”
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A KGE Based Knowledge Enhancing Method for Aspect-Level Sentiment Classification by Yu, Haibo, Lu, Guojun, Cai, Qianhua, Xue, Yun

“…ALSC (Aspect-level Sentiment Classification) is a fine-grained task in the field of NLP (Natural Language Processing) which aims to identify the sentiment…”
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A Semantic Enhancement Framework for Multimodal Sarcasm Detection by Zhong, Weiyu, Zhang, Zhengxuan, Wu, Qiaofeng, Xue, Yun, Cai, Qianhua

“…Sarcasm represents a language form where a discrepancy lies between the literal meanings and implied intention. Sarcasm detection is challenging with unimodal…”
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Multimodal Interaction and Fused Graph Convolution Network for Sentiment Classification of Online Reviews by Zeng, Dehong, Chen, Xiaosong, Song, Zhengxin, Xue, Yun, Cai, Qianhua

“…An increasing number of people tend to convey their opinions in different modalities. For the purpose of opinion mining, sentiment classification based on…”
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Syntactic Structure-Enhanced Dual Graph Convolutional Network for Aspect-Level Sentiment Classification by Chen, Jiehai, Qiu, Zhixun, Liu, Junxi, Xue, Yun, Cai, Qianhua

“…Aspect-level sentiment classification (ALSC) is a fine-grained sentiment analysis task that aims to predict the sentiment of the given aspect in a sentence…”
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A dual contrastive learning-based graph convolutional network with syntax label enhancement for aspect-based sentiment classification by Huang, Yuyan, Dai, Anan, Cao, Sha, Kuang, Qiuhua, Zhao, Hongya, Cai, Qianhua

“…Introduction: Aspect-based sentiment classification is a fine-grained sentiment classification task. State-of-the-art approaches in this field leverage graph…”
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Cross‐domain sequence labelling using language modelling and parameter generating by Zhou, Bo, Chen, Jianying, Cai, Qianhua, Xue, Yun, Yang, Chi, He, Jing

“…Sequence labelling (SL) tasks are currently widely studied in the field of natural language processing. Most sequence labelling methods are developed on a…”
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End-to-End Visual Grounding Framework for Multimodal NER in Social Media Posts by Lyu, Yifan, Hu, Jiapei, Xue, Yun, Cai, Qianhua

“…Multimodal named entity recognition (MNER) for social media aims to detect named entities in user-generated posts with the aid of visual information from…”
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BART-based contrastive and retrospective network for aspect-category-opinion-sentiment quadruple extraction by Xiong, Haoliang, Yan, Zehao, Wu, Chuhan, Lu, Guojun, Pang, Shiguan, Xue, Yun, Cai, Qianhua

“…Aspect-category-opinion-sentiment (ACOS) quadruple extraction is a fine-grained sentiment analysis task to extract full sentiment information, which aims to…”
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Relevance-aware visual entity filter network for multimodal aspect-based sentiment analysis by Chen, Yifan, Xiong, Haoliang, Li, Kuntao, Mai, Weixing, Xue, Yun, Cai, Qianhua, Li, Fenghuan

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A Novel Bi-Branch Graph Convolutional Neural Network for Aspect Level Sentiment Classification by Chen, Bingliang, Lu, Guojun, Xue, Yun, Cai, Qianhua

“…Aspect-level sentiment classification is a fine-grained task in sentiment analysis whose main purpose is to identify the sentiment polarity of a specific…”
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Sparse Variational Autoencoder-Based Interpretable Bimodal Word Embeddings by Tang, Jingyao, Zhong, Weiyu, Cai, Qianhua, Lu, Guojun, Yan, Zehao, Xue, Yun, Li, Xinguang

“…Word embedding is a basic task in the field of natural language processing, which is widely applied to a variety of tasks. In spite of delivering the semantic…”
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A Recommendation Algorithm Based On Fuzzy Clustering by Zhan, Huihui, Zhou, Weixing, Hu, Xiaohui, Cai, Qianhua, Zhang, Tao, Yang, Long

“…A recommendation algorithm based on fuzzy clustering is proposed in this paper. The idea of this system is as follows: Firstly, based on examples of hotel…”
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