DeepPatent: patent classification with convolutional neural networks and word embedding

2018 ◽  
Vol 117 (2) ◽  
pp. 721-744 ◽  
Author(s):  
Shaobo Li ◽  
Jie Hu ◽  
Yuxin Cui ◽  
Jianjun Hu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Word Embedding ◽  
Patent Classification

scholarly journals Multi-Channel Convolutional Neural Networks with Adversarial Training for Few-Shot Relation Classification (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13967-13968
Author(s):  
Yuxiang Xie ◽  
Hua Xu ◽  
Congcong Yang ◽  
Kai Gao
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Word Embedding ◽  
Vector Representation ◽  
Adversarial Learning ◽  
Model Experiments ◽  
Robust Model ◽  
Adversarial Training ◽  
Relation Classification

The distant supervised (DS) method has improved the performance of relation classification (RC) by means of extending the dataset. However, DS also brings the problem of wrong labeling. Contrary to DS, the few-shot method relies on few supervised data to predict the unseen classes. In this paper, we use word embedding and position embedding to construct multi-channel vector representation and use the multi-channel convolutional method to extract features of sentences. Moreover, in order to alleviate few-shot learning to be sensitive to overfitting, we introduce adversarial learning for training a robust model. Experiments on the FewRel dataset show that our model achieves significant and consistent improvements on few-shot RC as compared with baselines.

scholarly journals Text Classification Based on Convolutional Neural Networks and Word Embedding for Low-Resource Languages: Tigrinya

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 52
Author(s):  
Awet Fesseha ◽  
Shengwu Xiong ◽  
Eshete Derb Emiru ◽  
Moussa Diallo ◽  
Abdelghani Dahou
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Text Classification ◽  
Word Embedding ◽  
Northern Ethiopia ◽  
Classification Problems ◽  
Semitic Languages ◽  
Low Resource ◽  
Free Data ◽  
Class Labels

This article studies convolutional neural networks for Tigrinya (also referred to as Tigrigna), which is a family of Semitic languages spoken in Eritrea and northern Ethiopia. Tigrinya is a “low-resource” language and is notable in terms of the absence of comprehensive and free data. Furthermore, it is characterized as one of the most semantically and syntactically complex languages in the world, similar to other Semitic languages. To the best of our knowledge, no previous research has been conducted on the state-of-the-art embedding technique that is shown here. We investigate which word representation methods perform better in terms of learning for single-label text classification problems, which are common when dealing with morphologically rich and complex languages. Manually annotated datasets are used here, where one contains 30,000 Tigrinya news texts from various sources with six categories of “sport”, “agriculture”, “politics”, “religion”, “education”, and “health” and one unannotated corpus that contains more than six million words. In this paper, we explore pretrained word embedding architectures using various convolutional neural networks (CNNs) to predict class labels. We construct a CNN with a continuous bag-of-words (CBOW) method, a CNN with a skip-gram method, and CNNs with and without word2vec and FastText to evaluate Tigrinya news articles. We also compare the CNN results with traditional machine learning models and evaluate the results in terms of the accuracy, precision, recall, and F1 scoring techniques. The CBOW CNN with word2vec achieves the best accuracy with 93.41%, significantly improving the accuracy for Tigrinya news classification.

Improving bug localization with word embedding and enhanced convolutional neural networks

2019 ◽  
Vol 105 ◽  
pp. 17-29 ◽  
Author(s):  
Yan Xiao ◽  
Jacky Keung ◽  
Kwabena E. Bennin ◽  
Qing Mi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Word Embedding ◽  
Bug Localization

Traffic Lights Detection in adverse conditions using Convolutional Neural Networks

2018 ◽  
Author(s):  
George Symeonidis ◽  
Peter P. Groumpos ◽  
Evangelos Dermatas
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Lights ◽  
Adverse Conditions

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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