scholarly journals Joint Character-Level Word Embedding and Adversarial Stability Training to Defend Adversarial Text

2020 ◽  
Vol 34 (05) ◽  
pp. 8384-8391
Author(s):  
Hui Liu ◽  
Yongzheng Zhang ◽  
Yipeng Wang ◽  
Zheng Lin ◽  
Yige Chen
Keyword(s):  
Language Processing ◽  
Text Classification ◽  
State Of The Art ◽  
Word Embedding ◽  
Data Sets ◽  
Basic Task ◽  
Gradient Based ◽  
Adversarial Examples ◽  
Stability Training ◽  
Adversarial Example

Text classification is a basic task in natural language processing, but the small character perturbations in words can greatly decrease the effectiveness of text classification models, which is called character-level adversarial example attack. There are two main challenges in character-level adversarial examples defense, which are out-of-vocabulary words in word embedding model and the distribution difference between training and inference. Both of these two challenges make the character-level adversarial examples difficult to defend. In this paper, we propose a framework which jointly uses the character embedding and the adversarial stability training to overcome these two challenges. Our experimental results on five text classification data sets show that the models based on our framework can effectively defend character-level adversarial examples, and our models can defend 93.19% gradient-based adversarial examples and 94.83% natural adversarial examples, which outperforms the state-of-the-art defense models.

Learning emotional word embeddings for sentiment analysis

2021 ◽  
pp. 1-13
Author(s):  
Qingtian Zeng ◽  
Xishi Zhao ◽  
Xiaohui Hu ◽  
Hua Duan ◽  
Zhongying Zhao ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Research Problem ◽  
Emotional Word ◽  
Classification Model ◽  
Data Sets ◽  
Word Embeddings ◽  
Real World Data ◽  
Text Documents

Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models.

scholarly journals Enhancing the performance of cancer text classification model based on cancer hallmarks

2021 ◽  
Vol 10 (2) ◽  
pp. 316
Author(s):  
Noha Ali ◽  
Ahmed H. AbuEl-Atta ◽  
Hala H. Zayed
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Text Classification ◽  
State Of The Art ◽  
Classification Model ◽  
Biomedical Text ◽  
Cancer Hallmarks ◽  
Embedding Technique ◽  
Proposed Model ◽  
Biomedical Text Classification

<span id="docs-internal-guid-cb130a3a-7fff-3e11-ae3d-ad2310e265f8"><span>Deep learning (DL) algorithms achieved state-of-the-art performance in computer vision, speech recognition, and natural language processing (NLP). In this paper, we enhance the convolutional neural network (CNN) algorithm to classify cancer articles according to cancer hallmarks. The model implements a recent word embedding technique in the embedding layer. This technique uses the concept of distributed phrase representation and multi-word phrases embedding. The proposed model enhances the performance of the existing model used for biomedical text classification. The result of the proposed model overcomes the previous model by achieving an F-score equal to 83.87% using an unsupervised technique that trained on PubMed abstracts called PMC vectors (PMCVec) embedding. Also, we made another experiment on the same dataset using the recurrent neural network (RNN) algorithm with two different word embeddings Google news and PMCVec which achieving F-score equal to 74.9% and 76.26%, respectively.</span></span>

scholarly journals Dynamic Network Pruning with Interpretable Layerwise Channel Selection

2020 ◽  
Vol 34 (04) ◽  
pp. 6299-6306
Author(s):  
Yulong Wang ◽  
Xiaolu Zhang ◽  
Xiaolin Hu ◽  
Bo Zhang ◽  
Hang Su
Keyword(s):  
Prediction Accuracy ◽  
State Of The Art ◽  
Dynamic Network ◽  
Detection Algorithm ◽  
Network Pruning ◽  
Robust Model ◽  
Adversarial Examples ◽  
Adversarial Example ◽  
Decision Paths ◽  
Pruning Methods

Dynamic network pruning achieves runtime acceleration by dynamically determining the inference paths based on different inputs. However, previous methods directly generate continuous decision values for each weight channel, which cannot reflect a clear and interpretable pruning process. In this paper, we propose to explicitly model the discrete weight channel selections, which encourages more diverse weights utilization, and achieves more sparse runtime inference paths. Meanwhile, with the help of interpretable layerwise channel selections in the dynamic network, we can visualize the network decision paths explicitly for model interpretability. We observe that there are clear differences in the layerwise decisions between normal and adversarial examples. Therefore, we propose a novel adversarial example detection algorithm by discriminating the runtime decision features. Experiments show that our dynamic network achieves higher prediction accuracy under the similar computing budgets on CIFAR10 and ImageNet datasets compared to traditional static pruning methods and other dynamic pruning approaches. The proposed adversarial detection algorithm can significantly improve the state-of-the-art detection rate across multiple attacks, which provides an opportunity to build an interpretable and robust model.

scholarly journals Improved N-Best Extraction with an Evaluation on Language Data

2021 ◽  
pp. 1-35
Author(s):  
Johanna Björklund ◽  
Frank Drewes ◽  
Anna Jonsson
Keyword(s):  
Language Processing ◽  
State Of The Art ◽  
Search Space ◽  
Data Sets ◽  
Weighted Tree ◽  
Original Algorithm ◽  
Software Toolkit ◽  
Minimal Weight ◽  
Language Data ◽  
Memory Efficient

Abstract We show that a previously proposed algorithm for the N-best trees problem can be made more efficient by changing how it arranges and explores the search space. Given an integer N and a weighted tree automaton (wta) M over the tropical semiring, the algorithm computes N trees of minimal weight with respect to M. Compared to the original algorithm, the modifications increase the laziness of the evaluation strategy, which makes the new algorithm asymptotically more efficient than its predecessor. The algorithm is implemented in the software Betty, and compared to the state-of-the-art algorithm for extracting the N best runs, implemented in the software toolkit Tiburon. The data sets used in the experiments are wtas resulting from real-world natural language processing tasks, as well as artificially created wtas with varying degrees of nondeterminism. We find that Betty outperforms Tiburon on all tested data sets with respect to running time, while Tiburon seems to be the more memory-efficient choice.

scholarly journals Efficient processing of GRU based on word embedding for text classification

2019 ◽  
Vol 3 (4) ◽  
Author(s):  
Muhammad Zulqarnain ◽  
Rozaida Ghazali ◽  
Muhammad Ghulam Ghouse ◽  
Muhammad Faheem Mushtaq
Keyword(s):  
Language Processing ◽  
Text Classification ◽  
Classification Performance ◽  
Word Embedding ◽  
Training Data ◽  
Superior Performance ◽  
Sequential Data ◽  
Online Data ◽  
Benchmark Datasets ◽  
Recurrent Architecture

Text classification has become very serious problem for big organization to manage the large amount of online data and has been extensively applied in the tasks of Natural Language Processing (NLP). Text classification can support users to excellently manage and exploit meaningful information require to be classified into various categories for further use. In order to best classify texts, our research efforts to develop a deep learning approach which obtains superior performance in text classification than other RNNs approaches. However, the main problem in text classification is how to enhance the classification accuracy and the sparsity of the data semantics sensitivity to context often hinders the classification performance of texts. In order to overcome the weakness, in this paper we proposed unified structure to investigate the effects of word embedding and Gated Recurrent Unit (GRU) for text classification on two benchmark datasets included (Google snippets and TREC). GRU is a well-known type of recurrent neural network (RNN), which is ability of computing sequential data over its recurrent architecture. Experimentally, the semantically connected words are commonly near to each other in embedding spaces. First, words in posts are changed into vectors via word embedding technique. Then, the words sequential in sentences are fed to GRU to extract the contextual semantics between words. The experimental results showed that proposed GRU model can effectively learn the word usage in context of texts provided training data. The quantity and quality of training data significantly affected the performance. We evaluated the performance of proposed approach with traditional recurrent approaches, RNN, MV-RNN and LSTM, the proposed approach is obtained better results on two benchmark datasets in the term of accuracy and error rate.

scholarly journals Deepfake-Image Anti-Forensics with Adversarial Examples Attacks

2021 ◽  
Vol 13 (11) ◽  
pp. 288
Author(s):  
Li Fan ◽  
Wei Li ◽  
Xiaohui Cui
Keyword(s):  
State Of The Art ◽  
Poisson Noise ◽  
Future Research ◽  
Detection Accuracy ◽  
Generalization Performance ◽  
Image Forensic ◽  
Adversarial Examples ◽  
Adversarial Example ◽  
The Impact

Many deepfake-image forensic detectors have been proposed and improved due to the development of synthetic techniques. However, recent studies show that most of these detectors are not immune to adversarial example attacks. Therefore, understanding the impact of adversarial examples on their performance is an important step towards improving deepfake-image detectors. This study developed an anti-forensics case study of two popular general deepfake detectors based on their accuracy and generalization. Herein, we propose the Poisson noise DeepFool (PNDF), an improved iterative adversarial examples generation method. This method can simply and effectively attack forensics detectors by adding perturbations to images in different directions. Our attacks can reduce its AUC from 0.9999 to 0.0331, and the detection accuracy of deepfake images from 0.9997 to 0.0731. Compared with state-of-the-art studies, our work provides an important defense direction for future research on deepfake-image detectors, by focusing on the generalization performance of detectors and their resistance to adversarial example attacks.

scholarly journals A Causal Inference Method for Reducing Gender Bias in Word Embedding Relations

2020 ◽  
Vol 34 (05) ◽  
pp. 9434-9441
Author(s):  
Zekun Yang ◽  
Juan Feng
Keyword(s):  
Gender Bias ◽  
Language Processing ◽  
State Of The Art ◽  
Word Embedding ◽  
Coreference Resolution ◽  
Word Embeddings ◽  
Inference Method ◽  
Sentence Level ◽  
Statistical Dependency ◽  
And Gender

Word embedding has become essential for natural language processing as it boosts empirical performances of various tasks. However, recent research discovers that gender bias is incorporated in neural word embeddings, and downstream tasks that rely on these biased word vectors also produce gender-biased results. While some word-embedding gender-debiasing methods have been developed, these methods mainly focus on reducing gender bias associated with gender direction and fail to reduce the gender bias presented in word embedding relations. In this paper, we design a causal and simple approach for mitigating gender bias in word vector relation by utilizing the statistical dependency between gender-definition word embeddings and gender-biased word embeddings. Our method attains state-of-the-art results on gender-debiasing tasks, lexical- and sentence-level evaluation tasks, and downstream coreference resolution tasks.

scholarly journals Boosting Adversarial Attacks on Neural Networks with Better Optimizer

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Heng Yin ◽  
Hengwei Zhang ◽  
Jindong Wang ◽  
Ruiyu Dou
Keyword(s):  
Neural Networks ◽  
Success Rate ◽  
Gradient Descent ◽  
State Of The Art ◽  
Black Box ◽  
Security Threats ◽  
Gradient Descent Algorithm ◽  
Gradient Based ◽  
Adversarial Examples ◽  
Fast Gradient

Convolutional neural networks have outperformed humans in image recognition tasks, but they remain vulnerable to attacks from adversarial examples. Since these data are crafted by adding imperceptible noise to normal images, their existence poses potential security threats to deep learning systems. Sophisticated adversarial examples with strong attack performance can also be used as a tool to evaluate the robustness of a model. However, the success rate of adversarial attacks can be further improved in black-box environments. Therefore, this study combines a modified Adam gradient descent algorithm with the iterative gradient-based attack method. The proposed Adam iterative fast gradient method is then used to improve the transferability of adversarial examples. Extensive experiments on ImageNet showed that the proposed method offers a higher attack success rate than existing iterative methods. By extending our method, we achieved a state-of-the-art attack success rate of 95.0% on defense models.

scholarly journals Random Transformation of image brightness for adversarial attack

2021 ◽  
pp. 1-12
Author(s):  
Bo Yang ◽  
Kaiyong Xu ◽  
Hengjun Wang ◽  
Hengwei Zhang
Keyword(s):  
Success Rate ◽  
Data Augmentation ◽  
Black Box ◽  
Image Brightness ◽  
Gradient Based ◽  
Adversarial Examples ◽  
Random Transformation ◽  
Fast Gradient ◽  
Adversarial Example ◽  
Sign Method

Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding small, human-imperceptible perturbations to the original images, but make the model output inaccurate predictions. Before DNNs are deployed, adversarial attacks can thus be an important method to evaluate and select robust models in safety-critical applications. However, under the challenging black-box setting, the attack success rate, i.e., the transferability of adversarial examples, still needs to be improved. Based on image augmentation methods, this paper found that random transformation of image brightness can eliminate overfitting in the generation of adversarial examples and improve their transferability. In light of this phenomenon, this paper proposes an adversarial example generation method, which can be integrated with Fast Gradient Sign Method (FGSM)-related methods to build a more robust gradient-based attack and to generate adversarial examples with better transferability. Extensive experiments on the ImageNet dataset have demonstrated the effectiveness of the aforementioned method. Whether on normally or adversarially trained networks, our method has a higher success rate for black-box attacks than other attack methods based on data augmentation. It is hoped that this method can help evaluate and improve the robustness of models.

scholarly journals An Ensemble Learning Strategy for Eligibility Criteria Text Classification for Clinical Trial Recruitment: Algorithm Development and Validation

10.2196/17832 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e17832
Author(s):  
Kun Zeng ◽  
Zhiwei Pan ◽  
Yibin Xu ◽  
Yingying Qu
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Natural Language Processing ◽  
Ensemble Learning ◽  
Language Processing ◽  
Text Classification ◽  
State Of The Art ◽  
Shared Task ◽  
Eligibility Criteria ◽  
Short Text

Background Eligibility criteria are the main strategy for screening appropriate participants for clinical trials. Automatic analysis of clinical trial eligibility criteria by digital screening, leveraging natural language processing techniques, can improve recruitment efficiency and reduce the costs involved in promoting clinical research. Objective We aimed to create a natural language processing model to automatically classify clinical trial eligibility criteria. Methods We proposed a classifier for short text eligibility criteria based on ensemble learning, where a set of pretrained models was integrated. The pretrained models included state-of-the-art deep learning methods for training and classification, including Bidirectional Encoder Representations from Transformers (BERT), XLNet, and A Robustly Optimized BERT Pretraining Approach (RoBERTa). The classification results by the integrated models were combined as new features for training a Light Gradient Boosting Machine (LightGBM) model for eligibility criteria classification. Results Our proposed method obtained an accuracy of 0.846, a precision of 0.803, and a recall of 0.817 on a standard data set from a shared task of an international conference. The macro F1 value was 0.807, outperforming the state-of-the-art baseline methods on the shared task. Conclusions We designed a model for screening short text classification criteria for clinical trials based on multimodel ensemble learning. Through experiments, we concluded that performance was improved significantly with a model ensemble compared to a single model. The introduction of focal loss could reduce the impact of class imbalance to achieve better performance.

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