[PDF] Top 20 Using Convolutional Neural Networks to Classify Hate Speech
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Using Convolutional Neural Networks to Classify Hate Speech
... clusters using Logistic Regres- sion over a set of 860,071 tweets automatically annotated using a boot-strapping technique and supplemented with a dictionary of 339 offensive ...their ... See full document
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Speech Emotion Recognition Using Convolutional Recurrent Neural Networks with Attention Model
... investigate Speech Emotion Recognition (SER), which not only makes the communication between machine and human more natural and real, but also has great potential in the development of auxiliary technologies, such ... See full document
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UTFPR at SemEval 2019 Task 5: Hate Speech Identification with Recurrent Neural Networks
... One of the most common and effective strate- gies to tackle the problem of offensive language online is to train systems capable of recognizing such content. Several studies have been published in the last few years on ... See full document
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INF HatEval at SemEval 2019 Task 5: Convolutional Neural Networks for Hate Speech Detection Against Women and Immigrants on Twitter
... social networks, the task of detecting hate speech remains difficult, largely because of the use of different sets of data for work, lack of bench- marking, and efficient ...identify hate ... See full document
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Phone recognition with hierarchical convolutional deep maxout networks
... Deep convolutional neural networks (CNNs) have recently been shown to outperform fully connected deep neural networks (DNNs) both on low-resource and on large-scale speech ... See full document
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Deep Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas
... for neural network clas- sification, it is possible to arbitrarily change this cutoff to between 0 and 1 to alter the sensitivity and specificity of the ... See full document
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Using Convolutional Neural Networks for Sentiment Attitude Extraction from Analytical Texts
... and classify only those pairs of named entities that co-occur in the same sentence at least once in a document, which distance in words within pair was limited by k = ... See full document
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Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts
... impaired speech in Brazilian Portuguese, to allow a neuropsychological evaluation based on discourse ...(Recurrent Convolutional Neural Networks) which independently treat prosodic and textual ... See full document
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Interpreting Neural Network Hate Speech Classifiers
... Deep neural networks have been applied to hate speech detection with apparent success, but they have limited practical applicability without transparency into the predictions they ... See full document
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Impact of Earnings per Share on Market Price of Share with Special Reference to Selected Companies Listed on NSE
... deep neural networks, convolutional deep neural networks, deep belief networks and recurrent neural networks have been applied to fields like computer vision, ... See full document
5
Fungus Detection using Convolutional Neural Networks
... a convolutional neural network that help us to classify the fungus into categories abnormal image and normal ...pre-trained using transfer learning and identification is done using the ... See full document
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Pneumonia Detection Using Convolutional Neural Networks
... Radiograph of chest is penetrated through X-rays where the soft tissues produces a dark color and hard tissues like bones produces a bright color [2]. Patients diagnosed with pneumonia shows the chest cavity signs of ... See full document
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Spam detection in im images using convolutional neural networks
... precisely what this paper intends tom illustrate. As mentioned earlier in this paper, classifying emails has been a standard classification problem for ears. But we are no longer limited to emails anymore. Spammers have ... See full document
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Implementation of Smartbin using Convolutional Neural Networks
... of Convolutional Neural Networks (CNN) is ...accurately classify a wide variety of waste materials into their respective ...by using image augmentation ... See full document
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Combination of Multiple Acoustic Models with Multi-scale Features for Myanmar Speech Recognition
... automatic speech recognizer using deep convolutional neural networks ...automatic speech recognition due to its ability of reducing spectral variations and modelling spectral ... See full document
10
Deep Convolution Neural Networks for Automatic Eyeglasses Removal
... boosted using a larger training set, but the effect of big data is not as impressive as that shown in high-level vision ...three-layer networks in the following ... See full document
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JCTDHS at SemEval 2019 Task 5: Detection of Hate Speech in Tweets using Deep Learning Methods, Character N gram Features, and Preprocessing Methods
... of hate speech against immigrants and women in Twitter ...Detects Hate Speech) was developed for tweets written in ...features using the TF-IDF ... See full document
5
Phone Labeling Based on the Probabilistic Representation for Dysarthric Speech Recognition
... discuss speech recognition for persons with articulation disorders resulting from athetoid cerebral ...the speech style for a person with this type of articulation disorder is quite different from a ... See full document
5
EuSoMII Annual Meeting 2019 Book of abstracts
... a Convolutional Neural Network (CNN)-based binary classifier for brain tumor detection decreases as the degree of image quality degradation on brain MRI images ... See full document
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Application of Convolutional Neural Network to Classify Sitting and Standing Postures
... Similarly, the training accuracy increases steeply and plateaued around 1,000 iterations for standing posture, while the training accuracy shows only a slight increase for the case of sitting posture as shown in Fig.11. ... See full document
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