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Start Over Descriptor "dependency trees" Topic aspect-level sentiment classification
2 results on '"dependency trees"'

1. Aspect-level sentiment classification with aspect-opinion sentence pattern connection graph convolutional networks.

Author

Li, Hongye, Xu, Fuyong, Zhang, Zhiyu, Liu, Peiyu, and Zhang, Wenyin

Subjects
*CLASSIFICATION, *EMOTIONS, *TREES
Abstract

Attempting to identify and determine the sentiment polarity of one or more aspects (i.e., aspect words) in a sentence, Aspect-Level Sentiment Classification (abbreviated as ALSC) is a fine-grained sentiment classification task. Graph convolutional networks on dependency trees are now widely being used in related research to improve the accuracy of ALSC. The key to determining the polarity of aspectual emotions is to find the opinion, i.e., the opinion word, that is, most relevant to the aspectual emotion. However, in the dependency tree, a significant portion of aspect words and opinion words are not directly connected. And long-distance connections can lead to the model not paying enough attention to opinion words and losing information. In order to address this issue, by examining dependency syntactic structure and syntactic knowledge, we propose Aspect-opinion Sentence pattern Connection (ASC) to strengthen sentiment dependency graphs. We then develop the ASC-GCN to efficiently use the strengthened dependencies. Experimental results on four public benchmark datasets indicate that our approach achieves excellent performance on a lightweight model. [ABSTRACT FROM AUTHOR]

Published
2024
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2. Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics.

Author

Gou, Jianping, Du, Lan, Gou, Jianping, Ou, Weihua, and Zeng, Shaoning

Subjects
Computer science, Information technology industries, 3D reconstruction, ADMM, Aspect Level Sentiment Classification, C-MAPSS, Contrasitve Learning, DCNN-BiLSTM, Dempster-Shafer evidence theory, GAT, GCN, Graph Convolutional Networks, KGE, MMD, NMS, Soft-NMS, XSS attack, YOLOX, YoloV4, adversarial equilibrium, adversarial example, adversarial learning, anchor-free, anomaly detection, anti-noise performance, aspect-based sentiment analysis, aspect-level sentiment classification, attention mechanism, background matting, black-box attack, blind image deblurring, collaborative-representation-based classification, commonsense knowledge graph, computer vision, confidence score, contrastive learning, correlation filters, cost-weighted, cross-domain classification, cross-domain sentiment classification, cross-working, cyber-physical, data analysis, decoupling, deep learning, deep neural network, deep reinforcement learning, dependency trees, dependency types, discriminative feature learning, domain adaptation, elastic optical networks, end-to-end, ensemble attack, extension theory, external knowledge, face recognition, feature extraction, feature reuse, feature transformation, fine-tuning, fusion verification, fuzzy k-means, gait adjustment, garbage quantity identification, gated learning, geometric mean metric, graph attention mechanism, graph convolutional networks, graph neural networks, hate speech detection, head detection, hypergraph matching, image aesthetic assessment, image classification, image gradient orientations, image prior, image super-resolution, industrial control systems, information-theoretic metric learning, intelligent design, iterative majorization algorithm, joint semantic learning, kNN, knowledge distillation, knowledge graph embedding, label propagation, large-margin technique, license plate recognition, logarithm norm, low-high level joint task, machine learning, matrix nuclear norm, metric learning, mixed noise removal, models and algorithms, motion deblurring, multi-order attention, multi-output, multi-source domain adaptation, multi-task learning, multi-view stereo, multidimensional scaling, n/a, object detection, pairwise constraint propagation, payloads, pedestrian detection, people counting, plug-and-play, power load forecasting, rainy image recovery, robustness, routing, modulation and spectrum assignment, scheme design, second-order fitting, second-order gradient, semantic, semi-supervised learning, similarity metric, small sample, soft-NMS, sparse channel, sparsity, stability, state reconstruction, state-dependent switching, structure from motion, switched system, syntactic, temporal knowledge graph, time delay, traffic detection, transferability quantification, uncertain temporal knowledge graph, vehicle color recognition, vehicle re-identification, video surveillance, visual tracking, word embedding
Abstract

Summary: The present reprint contains 33 articles accepted and published in the Special Issue entitled "Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics, 2022" in the MDPI journal, Mathematics, which covers a wide range of topics connected to the theory and applications of feature representation learning for image processing, artificial intelligence, data mining and robotics. These topics include, among others, elements from image blurring, image aesthetic quality assessment, pedestrian detection, visual tracking, vehicle re-identification, face recognition, 3D reconstruction, the stability of switched systems, domain adaption, deep reinforcement, sentiment analysis, graph convolutional networks, knowledge graphs, geometric metric learning, etc. It is hoped that this reprint will be interesting and useful for those working in the area of image processing, computer vision, machine learning, natural language processing and robotics, as well as for those with backgrounds in machine learning who are willing to become familiar with recent advancements in artificial intelligence, which, today, is present in almost all aspects of human life and activities.

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