Transformation Networks for Target-Oriented Sentiment Classification

Li, Xin; Bing, Lidong; Lam, Wai; Shi, Bei

doi:10.18653/v1/p18-1087

Cited by 398 publications

(218 citation statements)

References 29 publications

Supporting

Mentioning

216

Contrasting

Unclassified

Order By: Relevance

“…In one of the previous studies, Transformation network [180], [181] also exploited the idea of attention in a simple way by incorporating it with convolution block, but the main problem was that attention modules in Transformation network are fixed and cannot adapt to changing circumstances. RAN was made efficient towards recognition of cluttered, complex, and noisy images by stacking multiple attention modules.…”

Section: Residual Attention Neural Networkmentioning

confidence: 99%

A survey of the recent architectures of deep convolutional neural networks

et al. 2020

View full text Add to dashboard Cite

Deep Convolutional Neural Networks (CNNs) are a special type of Neural Networks, which have shown exemplary performance on several competitions related to Computer Vision and Image Processing. Interesting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, Speech Recognition, etc. The powerful learning ability of deep CNN is largely due to the use of multiple feature extraction stages that can automatically learn representations from the data. Availability of a large amount of data and improvements in the hardware technology have accelerated the research in CNNs, and recently very interesting deep CNN architectures have been reported. In fact, several interesting ideas to bring advancements in CNNs have been explored such as the use of different activation and loss functions, parameter optimization, regularization, and architectural innovations. However, the major improvement in representational capacity of the deep CNN is achieved through architectural innovations. Especially, the idea of exploiting spatial and channel information, depth and width of architecture, and multi-path information processing has gained substantial attention. Similarly, the idea of using a block of layers as a structural unit is also gaining popularity. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention. Additionally, the elementary understanding of CNN components, current challenges and applications of CNN are also provided. CNNs are the best among learning algorithms in understanding images content, and have shown exemplary results in segmentation, classification, detection, and retrieval related tasks [8], [9]. The success of CNNs has captured attention beyond academia. In industry, companies such as Google, Microsoft, AT&T, NEC, and Facebook have developed active research groups for exploring new architectures of CNN [10]. At present, most of the frontrunners of image processing and computer vision competitions are employing deep CNN based models.The attractive feature of CNN is its ability to exploit spatial or time correlation of the data. The topology of CNN is divided into multiple learning stages composed of a combination of the convolutional layers, non-linear processing units, and subsampling layers [11]. CNNs are feedforward multilayered hierarchical networks that are similar to fully connected neural network where each layer, using a bank of convolutional kernels, performs multiple transformations [12]. Convolution operation extracts useful features from locally correlated data points. Output of the convolutional kernels is assigned to non-linear processing unit (activation function), which not only helps in learning abstractions but also emb...

show abstract

Section: Residual Attention Neural Networkmentioning

confidence: 99%

A survey of the recent architectures of deep convolutional neural networks

et al. 2020

View full text Add to dashboard Cite

show abstract

“…• HAST-TNet: HAST (Li et al 2018b) and TNet (Li et al 2018a) are the current state-of-the-art models on the tasks of target boundary detection and target sentiment classification respectively. HAST-TNet is the pipline approach of these two models.…”

Section: Compared Modelsmentioning

confidence: 99%

A Unified Model for Opinion Target Extraction and Target Sentiment Prediction

Bing

et al. 2019

AAAI

Self Cite

231

195

View full text Add to dashboard Cite

Target-based sentiment analysis involves opinion target extraction and target sentiment classification. However, most of the existing works usually studied one of these two sub-tasks alone, which hinders their practical use. This paper aims to solve the complete task of target-based sentiment analysis in an end-to-end fashion, and presents a novel unified model which applies a unified tagging scheme. Our framework involves two stacked recurrent neural networks: The upper one predicts the unified tags to produce the final output results of the primary target-based sentiment analysis; The lower one performs an auxiliary target boundary prediction aiming at guiding the upper network to improve the performance of the primary task. To explore the inter-task dependency, we propose to explicitly model the constrained transitions from target boundaries to target sentiment polarities. We also propose to maintain the sentiment consistency within an opinion target via a gate mechanism which models the relation between the features for the current word and the previous word. We conduct extensive experiments on three benchmark datasets and our framework achieves consistently superior results.

show abstract

“…As CNN can capture the informative n-grams features, convolutional memory networks were explored in [18] to incorporate an attention mechanism to sequentially compute the weights of multiple memory units corresponding to multi-words. Instead of attention networks, [5] proposed a component to generate target-specific representations for words, and employed a CNN layer as the feature extractor relying on a mechanism of preserving the original contextual information. Some other works [20] exploited human reading cognitive process for this task.…”

Section: Deep Methodsmentioning

confidence: 99%

“…A range of attention mechanisms are introduced to address this issue, such as target-to-sentence attention [2], fine-grained word-level attention [3], and multiple attentions [4]. Convolutional neural network (CNN)-based models are also recently used for this task because of the capability to extract the informative n-grams features [5]. All the aforementioned methods focus on exploiting labeled data to build the classification model, whose performance is often largely limited.…”

Section: Introductionmentioning

confidence: 99%

Learning Discriminative Neural Sentiment Units for Semi-supervised Target-Level Sentiment Classification

Jin

Yang

Pang

et al. 2020

Advances in Knowledge Discovery and Data Mining

View full text Add to dashboard Cite

Target-level sentiment classification aims at assigning sentiment polarities to opinion targets in a sentence, for which it is significantly more challenging to obtain large-scale labeled data than sentence/document-level sentiment classification due to the intricate contexts and relations of the target words. To address this challenge, we propose a novel semi-supervised approach to learn sentiment-aware representations from easily accessible unlabeled data specifically for the finegrained sentiment learning. This is very different from current popular semi-supervised solutions that use the unlabeled data via pretraining to generate generic representations for various types of downstream tasks. Particularly, we show for the first time that we can learn and detect some highly sentiment-discriminative neural units from the unsupervised pretrained model, termed neural sentiment units. Due to the discriminability, these sentiment units can be leveraged by downstream LSTM-based classifiers to generate sentiment-aware and context-dependent word representations to substantially improve their sentiment classification performance. Extensive empirical results on two benchmark datasets show that our approach (i) substantially outperforms state-of-the-art sentiment classifiers and (ii) achieves significantly better data efficiency.

show abstract

Transformation Networks for Target-Oriented Sentiment Classification

Cited by 398 publications

References 29 publications

A survey of the recent architectures of deep convolutional neural networks

A survey of the recent architectures of deep convolutional neural networks

A Unified Model for Opinion Target Extraction and Target Sentiment Prediction

Learning Discriminative Neural Sentiment Units for Semi-supervised Target-Level Sentiment Classification

Contact Info

Product

Resources

About