scholarly journals A Multichannel Model for Microbial Key Event Extraction Based on Feature Fusion and Attention Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Peng Li ◽  
Qian Wang
Keyword(s):  
Sentiment Analysis ◽  
Feature Fusion ◽  
Attention Mechanism ◽  
Event Extraction ◽  
Semantic Features ◽  
Analysis Model ◽  
Global Features ◽  
Public Health Emergencies ◽  
Multimodal Information ◽  
Local And Global Features

In order to further mine the deep semantic information of the microbial text of public health emergencies, this paper proposes a multichannel microbial sentiment analysis model MCMF-A. Firstly, we use word2vec and fastText to generate word vectors in the feature vector embedding layer and fuse them with lexical and location feature vectors; secondly, we build a multichannel layer based on CNN and BiLSTM to extract local and global features of the microbial text; then we build an attention mechanism layer to extract the important semantic features of the microbial text; thirdly, we merge the multichannel output in the fusion layer and use soft; finally, the results are merged in the fusion layer, and a surtax function is used in the output layer for sentiment classification. The results show that the F1 value of the MCMF-A sentiment analysis model reaches 90.21%, which is 9.71% and 9.14% higher than the benchmark CNN and BiLSTM models, respectively. The constructed dataset is small in size, and the multimodal information such as images and speech has not been considered.

A Multi-Classification Sentiment Analysis Model of Chinese Short Text Based on Gated Linear Units and Attention Mechanism

2021 ◽  
Vol 20 (6) ◽  
pp. 1-13
Author(s):  
Lei Liu ◽  
Hao Chen ◽  
Yinghong Sun
Keyword(s):  
Sentiment Analysis ◽  
Local Features ◽  
Attention Mechanism ◽  
Semantic Features ◽  
Analysis Model ◽  
Short Text ◽  
Sentiment Lexicon ◽  
Benchmark Datasets ◽  
Convolution Kernels ◽  
Multi Classification

Sentiment analysis of social media texts has become a research hotspot in information processing. Sentiment analysis methods based on the combination of machine learning and sentiment lexicon need to select features. Selected emotional features are often subjective, which can easily lead to overfitted models and poor generalization ability. Sentiment analysis models based on deep learning can automatically extract effective text emotional features, which will greatly improve the accuracy of text sentiment analysis. However, due to the lack of a multi-classification emotional corpus, it cannot accurately express the emotional polarity. Therefore, we propose a multi-classification sentiment analysis model, GLU-RCNN, based on Gated Linear Units and attention mechanism. Our model uses the Gated Linear Units based attention mechanism to integrate the local features extracted by CNN with the semantic features extracted by the LSTM. The local features of short text are extracted and concatenated by using multi-size convolution kernels. At the classification layer, the emotional features extracted by CNN and LSTM are respectively concatenated to express the emotional features of the text. The detailed evaluation on two benchmark datasets shows that the proposed model outperforms state-of-the-art approaches.

scholarly journals Text Sentiment Classification Based on Feature Fusion

2020 ◽  
Vol 34 (4) ◽  
pp. 515-520
Author(s):  
Chen Zhang ◽  
Qingxu Li ◽  
Xue Cheng
Keyword(s):  
Hybrid Model ◽  
Short Term Memory ◽  
Feature Fusion ◽  
Contextual Information ◽  
Sentiment Classification ◽  
Global Features ◽  
Softmax Classifier ◽  
Parallel Hybrid ◽  
Local And Global Features ◽  
Lstm Network

The convolutional neural network (CNN) and long short-term memory (LSTM) network are adept at extracting local and global features, respectively. Both can achieve excellent classification effects. However, the CNN performs poorly in extracting the global contextual information of the text, while LSTM often overlooks the features hidden between words. For text sentiment classification, this paper combines the CNN with bidirectional LSTM (BiLSTM) into a parallel hybrid model called CNN_BiLSTM. Firstly, the CNN was adopted to extract the local features of the text quickly. Next, the BiLSTM was employed to obtain the global text features containing contextual semantics. After that, the features extracted by the two neural networks (NNs) were fused, and processed by Softmax classifier for text sentiment classification. To verify its performance, the CNN_BiLSTM was compared with single NNs like CNN and LSTM, as well as other deep learning (DL) NNs through experiments. The experimental results show that the proposed parallel hybrid model outperformed the contrastive methods in F1-score and accuracy. Therefore, our model can solve text sentiment classification tasks effectively, and boast better practical value than other NNs.

scholarly journals Chinese Event Extraction Based on Attention and Semantic Features: A Bidirectional Circular Neural Network

2018 ◽  
Vol 10 (10) ◽  
pp. 95 ◽  
Author(s):  
Yue Wu ◽  
Junyi Zhang
Keyword(s):  
Neural Network ◽  
Attention Mechanism ◽  
Semantic Feature ◽  
Word Embedding ◽  
Event Extraction ◽  
Semantic Features ◽  
Improve Performance

Chinese event extraction uses word embedding to capture similarity, but suffers when handling previously unseen or rare words. From the test, we know that characters may provide some information that we cannot obtain in words, so we propose a novel architecture for combining word representations: character–word embedding based on attention and semantic features. By using an attention mechanism, our method is able to dynamically decide how much information to use from word or character level embedding. With the semantic feature, we can obtain some more information about a word from the sentence. We evaluate different methods on the CEC Corpus, and this method is found to improve performance.

scholarly journals Multi-scale Adaptive Feature Fusion Network for Semantic Segmentation in Remote Sensing Images

2020 ◽  
Vol 12 (5) ◽  
pp. 872 ◽  
Author(s):  
Ronghua Shang ◽  
Jiyu Zhang ◽  
Licheng Jiao ◽  
Yangyang Li ◽  
Naresh Marturi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Multiple Scales ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Semantic Features ◽  
Remote Sensing Images ◽  
Global Features ◽  
Global Average ◽  
Multi Scale ◽  
Context Extraction

Semantic segmentation of high-resolution remote sensing images is highly challenging due to the presence of a complicated background, irregular target shapes, and similarities in the appearance of multiple target categories. Most of the existing segmentation methods that rely only on simple fusion of the extracted multi-scale features often fail to provide satisfactory results when there is a large difference in the target sizes. Handling this problem through multi-scale context extraction and efficient fusion of multi-scale features, in this paper we present an end-to-end multi-scale adaptive feature fusion network (MANet) for semantic segmentation in remote sensing images. It is a coding and decoding structure that includes a multi-scale context extraction module (MCM) and an adaptive fusion module (AFM). The MCM employs two layers of atrous convolutions with different dilatation rates and global average pooling to extract context information at multiple scales in parallel. MANet embeds the channel attention mechanism to fuse semantic features. The high- and low-level semantic information are concatenated to generate global features via global average pooling. These global features are used as channel weights to acquire adaptive weight information of each channel by the fully connected layer. To accomplish an efficient fusion, these tuned weights are applied to the fused features. Performance of the proposed method has been evaluated by comparing it with six other state-of-the-art networks: fully convolutional networks (FCN), U-net, UZ1, Light-weight RefineNet, DeepLabv3+, and APPD. Experiments performed using the publicly available Potsdam and Vaihingen datasets show that the proposed MANet significantly outperforms the other existing networks, with overall accuracy reaching 89.4% and 88.2%, respectively and with average of F1 reaching 90.4% and 86.7% respectively.

Fine reconstruction of underwater images for environmental feature fusion

2021 ◽  
Vol 18 (5) ◽  
pp. 172988142110396
Author(s):  
Tao Xu ◽  
Jiyong Zhou ◽  
Wentao Guo ◽  
Lei Cai ◽  
Yukun Ma
Keyword(s):  
Image Reconstruction ◽  
Feature Fusion ◽  
Image Data ◽  
Target Feature ◽  
Global Features ◽  
Data Set ◽  
Underwater Image ◽  
Reconstruction Methods ◽  
Local And Global Features ◽  
Underwater Target

Complicated underwater environments, such as occlusion by foreign objects and dim light, causes serious loss of underwater targets feature. Furthermore, underwater ripples cause target deformation, which greatly increases the difficulty of feature extraction. Then, existing image reconstruction models cannot effectively achieve target reconstruction due to insufficient underwater target features, and there is a blurred texture in the reconstructed area. To solve the above problems, a fine reconstruction of underwater images with the target feature missing from the environment feature was proposed. Firstly, the salient features of underwater images are obtained in terms of positive and negative sample learning. Secondly, a layered environmental attention mechanism is proposed to retrieve the relevant local and global features in the context. Finally, a coarse-to-fine image reconstruction model, with gradient penalty constraints, is constructed to obtain the fine restoration results. Contrast experiment between the proposed algorithm and the existing image reconstruction methods has been done in stereo quantitative underwater image data set, real-world underwater image enhancement data set, and underwater image data set, clearly proving that the proposed one is more effective and superior.

Fully Convolutional DenseNet with Attention Mechanism for Liver Lesion Segmentation in MRI Images

2021 ◽  
Vol 11 (8) ◽  
pp. 2231-2242
Author(s):  
Fei Gao ◽  
Kai Qiao ◽  
Jinjin Hai ◽  
Bin Yan ◽  
Minghui Wu ◽  
...  
Keyword(s):  
Liver Tumor ◽  
Feature Fusion ◽  
Liver Tumors ◽  
Contextual Information ◽  
Liver Lesion ◽  
Attention Mechanism ◽  
Tumor Segmentation ◽  
Semantic Features ◽  
Lesion Segmentation ◽  
High Level

The goal of this research is to achieve accurate segmentation of liver tumors in noncontrast T2-weighted magnetic resonance imaging. As liver tumors and adjacent organs are represented by pixels of very similar gray intensity, segmentation is challenging, and the presence of different sizes of liver tumor makes segmentation more difficult. Differing from previous work to capture contextual information using multiscale feature fusion with concatenation, attention mechanism is added to our segmentation model to extract precise global contextual information for pixel labeling without requiring complex dilated convolution. This study describe a liver lesion segmentation model derived from FC-DenseNet with attention mechanism. Specifically, a global attention module (GAM) is added to up-sampling path, and high-level features are processed by the GAM to generating weighting information for guiding high resolution detail features recovery. High-level features are very effective for accurate category classification, but relatively weak at pixel classification and predicting restoration of the original resolution, so the fusion of high-level semantic features and low-level detail features can improve segmentation accuracy. A weighted focal loss function is used to solve the problem of lesion area occupying a relatively low proportion of the whole image, and to deal with the disequilibrium of foreground and background in the training liver lesion images. Experimental results show our segmentation model can automatically segment liver tumors from complete MRI images, and the addition of the GAM model can effectively improve liver tumor segmentation. Our algorithms have obvious advantages over other CNN algorithms and traditional manual methods of feature extraction.

scholarly journals Convolutional Attention Network with Maximizing Mutual Information for Fine-Grained Image Classification

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1511
Author(s):  
Fenglei Wang ◽  
Hao Zhou ◽  
Shuohao Li ◽  
Jun Lei ◽  
Jun Zhang
Keyword(s):  
Mutual Information ◽  
Image Classification ◽  
Semantic Features ◽  
Classification Methods ◽  
Global Features ◽  
Fine Grained ◽  
Symmetric Structure ◽  
Learning Techniques ◽  
Local And Global Features ◽  
Image Pairs

Fine-grained image classification has seen a great improvement benefiting from the advantages of deep learning techniques. Most fine-grained image classification methods focus on extracting discriminative features and combining the global features with the local ones. However, the accuracy is limited due to the inter-class similarity and the inner-class divergence as well as the lack of enough labelled images to train a deep network which can generalize to fine-grained classes. To deal with these problems, we develop an algorithm which combines Maximizing the Mutual Information (MMI) with the Learning Attention (LA). We make use of MMI to distill knowledge from the image pairs which contain the same object. Meanwhile we take advantage of the LA mechanism to find the salient region of the image to enhance the information distillation. Our model can extract more discriminative semantic features and improve the performance on fine-grained image classification. Our model has a symmetric structure, in which the paired images are inputted into the same network to extract the local and global features for the subsequent MMI and LA modules. We train the model by maximizing the mutual information and minimizing the cross-entropy stage by stage alternatively. Experiments show that our model can improve the performance of the fine-grained image classification effectively.

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