scholarly journals Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction

2020 ◽  
Vol 34 (05) ◽  
pp. 9298-9305
Author(s):  
Zhen Wu ◽  
Fei Zhao ◽  
Xin-Yu Dai ◽  
Shujian Huang ◽  
Jiajun Chen
Keyword(s):  
Neural Network ◽  
Transfer Process ◽  
State Of The Art ◽  
Transformation Method ◽  
Sentiment Classification ◽  
Experimental Results ◽  
Online Review ◽  
Neural Models ◽  
Network Methods ◽  
Novel Model

Target-oriented opinion words extraction (TOWE) is a new subtask of ABSA, which aims to extract the corresponding opinion words for a given opinion target in a sentence. Recently, neural network methods have been applied to this task and achieve promising results. However, the difficulty of annotation causes the datasets of TOWE to be insufficient, which heavily limits the performance of neural models. By contrast, abundant review sentiment classification data are easily available at online review sites. These reviews contain substantial latent opinions information and semantic patterns. In this paper, we propose a novel model to transfer these opinions knowledge from resource-rich review sentiment classification datasets to low-resource task TOWE. To address the challenges in the transfer process, we design an effective transformation method to obtain latent opinions, then integrate them into TOWE. Extensive experimental results show that our model achieves better performance compared to other state-of-the-art methods and significantly outperforms the base model without transferring opinions knowledge. Further analysis validates the effectiveness of our model.

scholarly journals A recommendations model with multiaspect awareness and hierarchical user-product attention mechanisms

2020 ◽  
Vol 17 (3) ◽  
pp. 849-865
Author(s):  
Zhongqin Bi ◽  
Shuming Dou ◽  
Zhe Liu ◽  
Yongbin Li
Keyword(s):  
State Of The Art ◽  
Weight Vector ◽  
User Preferences ◽  
The Other ◽  
Attention Networks ◽  
Proposed Model ◽  
Network Methods ◽  
Public Datasets ◽  
Novel Model ◽  
Attention Weight

Neural network methods have been trained to satisfactorily learn user/product representations from textual reviews. A representation can be considered as a multiaspect attention weight vector. However, in several existing methods, it is assumed that the user representation remains unchanged even when the user interacts with products having diverse characteristics, which leads to inaccurate recommendations. To overcome this limitation, this paper proposes a novel model to capture the varying attention of a user for different products by using a multilayer attention framework. First, two individual hierarchical attention networks are used to encode the users and products to learn the user preferences and product characteristics from review texts. Then, we design an attention network to reflect the adaptive change in the user preferences for each aspect of the targeted product in terms of the rating and review. The results of experiments performed on three public datasets demonstrate that the proposed model notably outperforms the other state-of-the-art baselines, thereby validating the effectiveness of the proposed approach.

scholarly journals Research on Sentiment Classification Algorithms on Online Review

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Ruixia Yan ◽  
Zhijie Xia ◽  
Yanxi Xie ◽  
Xiaoli Wang ◽  
Zukang Song
Keyword(s):  
Neural Network ◽  
Sentiment Classification ◽  
Online Review ◽  
Support Vector ◽  
Classification Algorithms ◽  
Evaluation Indexes ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Reinforcement Learning Models

The product online review text contains a large number of opinions and emotions. In order to identify the public’s emotional and tendentious information, we present reinforcement learning models in which sentiment classification algorithms of product online review corpus are discussed in this paper. In order to explore the classification effect of different sentiment classification algorithms, we conducted a research on Naive Bayesian algorithm, support vector machine algorithm, and neural network algorithm and carried out some comparison using a concrete example. The evaluation indexes and the three algorithms are compared in different lengths of sentence and word vector dimensions. The results present that neural network algorithm is effective in the sentiment classification of product online review corpus.

scholarly journals Sentiment Classification Using Convolutional Neural Networks

2019 ◽  
Vol 9 (11) ◽  
pp. 2347 ◽  
Author(s):  
Hannah Kim ◽  
Young-Seob Jeong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Text Classification ◽  
State Of The Art ◽  
Sentiment Classification ◽  
Learning Models ◽  
Text Data ◽  
Textual Data ◽  
Better Than

As the number of textual data is exponentially increasing, it becomes more important to develop models to analyze the text data automatically. The texts may contain various labels such as gender, age, country, sentiment, and so forth. Using such labels may bring benefits to some industrial fields, so many studies of text classification have appeared. Recently, the Convolutional Neural Network (CNN) has been adopted for the task of text classification and has shown quite successful results. In this paper, we propose convolutional neural networks for the task of sentiment classification. Through experiments with three well-known datasets, we show that employing consecutive convolutional layers is effective for relatively longer texts, and our networks are better than other state-of-the-art deep learning models.

scholarly journals Video Person Re-ID: Fantastic Techniques and Where to Find Them (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13893-13894
Author(s):  
Priyank Pathak ◽  
Amir Erfan Eshratifar ◽  
Michael Gormish
Keyword(s):  
Neural Network ◽  
Loss Function ◽  
State Of The Art ◽  
Source Code ◽  
Status Quo ◽  
Current Status ◽  
Neural Models ◽  
Academic Interest ◽  
Multiple Camera ◽  
The Common

The ability to identify the same person from multiple camera views without the explicit use of facial recognition is receiving commercial and academic interest. The current status-quo solutions are based on attention neural models. In this paper, we propose Attention and CL loss, which is a hybrid of center and Online Soft Mining (OSM) loss added to the attention loss on top of a temporal attention-based neural network. The proposed loss function applied with bag-of-tricks for training surpasses the state of the art on the common person Re-ID datasets, MARS and PRID 2011. Our source code is publicly available on github1.

scholarly journals Aspect-Based Sentiment Classification with Attentive Neural Turing Machines

2019 ◽  
Author(s):  
Qianren Mao ◽  
Jianxin Li ◽  
Senzhang Wang ◽  
Yuanning Zhang ◽  
Hao Peng ◽  
...  
Keyword(s):  
State Of The Art ◽  
Semantic Context ◽  
Sentiment Classification ◽  
Memory Storage ◽  
External Memory ◽  
Turing Machines ◽  
Target Information ◽  
Input And Output ◽  
Memory Content ◽  
Novel Model

Aspect-based sentiment classification aims to identify sentiment polarity expressed towards a given opinion target in a sentence. The sentiment polarity of the target is not only highly determined by sentiment semantic context but also correlated with the concerned opinion target. Existing works cannot effectively capture and store the inter-dependence between the opinion target and its context. To solve this issue, we propose a novel model of Attentive Neural Turing Machines (ANTM). Via interactive read-write operations between an external memory storage and a recurrent controller, ANTM can learn the dependable correlation of the opinion target to context and concentrate on crucial sentiment information. Specifically, ANTM separates the information of storage and computation, which extends the capabilities of the controller to learn and store sequential features. The read and write operations enable ANTM to adaptively keep track of the interactive attention history between memory content and controller state. Moreover, we append target entity embeddings into both input and output of the controller in order to augment the integration of target information. We evaluate our model on SemEval2014 dataset which contains reviews of Laptop and Restaurant domains and Twitter review dataset. Experimental results verify that our model achieves state-of-the-art performance on aspect-based sentiment classification.

scholarly journals Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

2021 ◽  
Vol 9 ◽  
Author(s):  
Shui-Hua Wang ◽  
Suresh Chandra Satapathy ◽  
Donovan Anderson ◽  
Shi-Xin Chen ◽  
Yu-Dong Zhang
Keyword(s):  
Neural Network ◽  
Data Augmentation ◽  
State Of The Art ◽  
Model Averaging ◽  
Community Acquired Pneumonia ◽  
Test Set ◽  
Max Pooling ◽  
L2 Norm ◽  
Healthy Control ◽  
Novel Model

Aim: Coronavirus disease 2019 (COVID-19) is a form of disease triggered by a new strain of coronavirus. This paper proposes a novel model termed “deep fractional max pooling neural network (DFMPNN)” to diagnose COVID-19 more efficiently.Methods: This 12-layer DFMPNN replaces max pooling (MP) and average pooling (AP) in ordinary neural networks with the help of a novel pooling method called “fractional max-pooling” (FMP). In addition, multiple-way data augmentation (DA) is employed to reduce overfitting. Model averaging (MA) is used to reduce randomness.Results: We ran our algorithm on a four-category dataset that contained COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis (SPT), and healthy control (HC). The 10 runs on the test set show that the micro-averaged F1 (MAF) score of our DFMPNN is 95.88%.Discussions: This proposed DFMPNN is superior to 10 state-of-the-art models. Besides, FMP outperforms traditional MP, AP, and L2-norm pooling (L2P).

scholarly journals Temporal Motionless Analysis of Video using CNN in MPSoC

2020 ◽  
Author(s):  
Somdip Dey ◽  
Amit Singh ◽  
Dilip Kumar Prasad ◽  
Klaus D. Mcdonald-Maier
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Training Image ◽  
Experimental Results ◽  
Current Frame ◽  
Previous Image

<div><div><div><p>This paper proposes a novel human-inspired methodology called IRON-MAN (Integrated RatiONal prediction and Motionless ANalysis of videos) on mobile multi-processor systems-on-chips (MPSoCs). The methodology integrates analysis of the previous image frames of the video to represent the analysis of the current frame in order to perform Temporal Motionless Analysis of the Video (TMAV). This is the first work on TMAV using Convolutional Neural Network (CNN) for scene prediction in MPSoCs. Experimental results show that our methodology outperforms state-of-the-art. We also introduce a metric named, Energy Consumption per Training Image (ECTI) to assess the suitability of using a CNN model in mobile MPSoCs with a focus on energy consumption of the device.</p></div></div></div>

scholarly journals DoPAMINE: Double-Sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling

2019 ◽  
Vol 33 ◽  
pp. 4031-4038
Author(s):  
Sunghwan Joo ◽  
Sungmin Cha ◽  
Taesup Moon
Keyword(s):  
Neural Network ◽  
Multiplicative Noise ◽  
Additive Noise ◽  
State Of The Art ◽  
Unbiased Estimate ◽  
Experimental Results ◽  
Fine Tuning ◽  
Noisy Image ◽  
Supervised Training ◽  
The Given

We propose DoPAMINE, a new neural network based multiplicative noise despeckling algorithm. Our algorithm is inspired by Neural AIDE (N-AIDE), which is a recently proposed neural adaptive image denoiser. While the original NAIDE was designed for the additive noise case, we show that the same framework, i.e., adaptively learning a network for pixel-wise affine denoisers by minimizing an unbiased estimate of MSE, can be applied to the multiplicative noise case as well. Moreover, we derive a double-sided masked CNN architecture which can control the variance of the activation values in each layer and converge fast to high denoising performance during supervised training. In the experimental results, we show our DoPAMINE possesses high adaptivity via fine-tuning the network parameters based on the given noisy image and achieves significantly better despeckling results compared to SAR-DRN, a state-of-the-art CNN-based algorithm.

scholarly journals LCF: A Local Context Focus Mechanism for Aspect-Based Sentiment Classification

2019 ◽  
Vol 9 (16) ◽  
pp. 3389 ◽  
Author(s):  
Biqing Zeng ◽  
Heng Yang ◽  
Ruyang Xu ◽  
Wu Zhou ◽  
Xuli Han
Keyword(s):  
State Of The Art ◽  
Sentiment Classification ◽  
Experimental Results ◽  
Local Context ◽  
Baseline Model ◽  
Global Context ◽  
Benchmark Datasets ◽  
Art Performance ◽  
Context Features

Aspect-based sentiment classification (ABSC) aims to predict sentiment polarities of different aspects within sentences or documents. Many previous studies have been conducted to solve this problem, but previous works fail to notice the correlation between the aspect’s sentiment polarity and the local context. In this paper, a Local Context Focus (LCF) mechanism is proposed for aspect-based sentiment classification based on Multi-head Self-Attention (MHSA). This mechanism is called LCF design, and utilizes the Context features Dynamic Mask (CDM) and Context Features Dynamic Weighted (CDW) layers to pay more attention to the local context words. Moreover, a BERT-shared layer is adopted to LCF design to capture internal long-term dependencies of local context and global context. Experiments are conducted on three common ABSC datasets: the laptop and restaurant datasets of SemEval-2014 and the ACL twitter dataset. Experimental results demonstrate that the LCF baseline model achieves considerable performance. In addition, we conduct ablation experiments to prove the significance and effectiveness of LCF design. Especially, by incorporating with BERT-shared layer, the LCF-BERT model refreshes state-of-the-art performance on all three benchmark datasets.

scholarly journals Metal artifact reduction on MRI images

2018 ◽  
Vol 226 ◽  
pp. 04048
Author(s):  
Nikolay V. Gapon ◽  
Evgenii A. Semenishchev ◽  
Oxana S. Balabaeva ◽  
Arina A. Skorikova ◽  
Olga A. Tokareva ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
State Of The Art ◽  
Geometric Model ◽  
Similarity Criterion ◽  
Experimental Results ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Trained Neural Network

This article examines the method of image reconstruction, which aims to restore the exposed areas on MRI images. The algorithm is based on a geometric model for patch synthesis. The lost pixels are recovered by copying pixel values from the source using a similarity criterion. We used a trained neural network to choose the “best similar” patch. Experimental results show that the proposed method outperforms widely used state-of-the-art methods.

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