[PDF] Top 20 The generalization performance of hate speech detection using machine learning
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The generalization performance of hate speech detection using machine learning
... of hate speech [2]. Hate speech is defined as speech that attacks a person or a group based on attributes such as race, religion, ethnic origin, national origin, sex, disability, sexual ... See full document
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CIC at SemEval 2019 Task 5: Simple Yet Very Efficient Approach to Hate Speech Detection, Aggressive Behavior Detection, and Target Classification in Twitter
... the detection of (A) Hate speech against immigrants and women, (B) Aggressive behavior and target classification, both for English and Spanish languages at Hateval ...vectors using various ... See full document
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UVA Wahoos at SemEval 2019 Task 6: Hate Speech Identification using Ensemble Machine Learning
... the Hate Speech Detection data set used in Malmasi and Zampieri (2017) where the authors perform a three way classification - Hate Speech, Offensive and ...identifying hate ... See full document
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Ghmerti at SemEval 2019 Task 6: A Deep Word and Character based Approach to Offensive Language Identification
... the Hate Speech Detection dataset and experiments with different machine learning models, such as logistic regression, na¨ıve Bayes, random forests, and lin- ear SVMs to investigate ... See full document
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Improving Hate Speech Detection with Deep Learning Ensembles
... For our experiments we utilize Python neural network and machine learning libraries. Specifically, Scikit-Learn (Pe- dregosa et al., 2011) is used to create feature representa- tions for input to ... See full document
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Combining Shallow and Deep Learning for Aggressive Text Detection
... the detection of aggressive speech (or its many subtypes) rely on tradi- tional text classification techniques, such as the naive Bayes classifier (Kwok and Wang, 2013; Chen et ...cyberbullying using ... See full document
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The Risk of Racial Bias in Hate Speech Detection
... text using an adapted version of the script for Twitter GloVe ...with using ELMo embeddings, but found that they did not boost performance for this ...models using Adam with a learning ... See full document
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A Survey on Hate Speech Detection using Natural Language Processing
... on hate speech detection, ranging from around 100 labelled comments used in the knowledge-based work by Dinakar et ...annotating hate speech is an extremely time consuming endeavour: ... See full document
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HATEMINER at SemEval 2019 Task 5: Hate speech detection against Immigrants and Women in Twitter using a Multinomial Naive Bayes Classifier
... detect hate speech against two specific targets, immigrants and ...Of Speech features to further investigate the performance of these clas- ...deep learning approaches respond to this ... See full document
5
Leveraging Intra User and Inter User Representation Learning for Automated Hate Speech Detection
... els by Kim (2014), and a N-gram model (Waseem and Hovy, 2016). We evaluate these models on three metrics: precision, recall and F1 score. The results are shown in Table 1. We report results for | U | = 100 in Table 1, as ... See full document
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A Pragmatic Supervised Learning Methodology of Hate Speech Detection in Social Media
... in hate speech automatic detection and related tasks ...classifiers’ performance because it incorporates at some degree the context of each ...of using words it is also possible to use ... 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
... a machine learning based method to detect hate speech on online user comments from two ...(hate speech, derogatory, ...categories: hate speech, offensive language, ... See full document
5
Predictive Embeddings for Hate Speech Detection on Twitter
... data using Spacy (Honnibal and Johnson, ...ter performance without lemmatization and lower- casing (see supplement for ...train using RMSprop with a learning rate of ... See full document
7
A survey of intrusion detection technique using various technique of machine learning
... intrusion detection technique using machine learning and feature optimization technique have been ...out using neural network techniques, genetic algorithm and particle of swarm ... See full document
5
ASD (Autism Spectrum Disorder): Early Detection Intervention using Machine Learning
... The main focus of the system is to create a mobile application for video raters specifically doctors from which they can assess 30 behavioral features. The rater will open the application, record the video and answer a ... See full document
7
Stress Level Detection from Human Speech Using Machine Learning Techniques
... Paper 4 shows that the K-NN classifier classifies 4 emotions viz. anger, joy, sorrow, neutral based on features such as pitch, energy, entropy and MFCC. Both, paper 2 and 4 use the EMO database for feature extraction. ... See full document
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Impact of Different Random Initializations on Generalization Performance of Extreme Learning Machine
... Extreme learning machine (ELM) [1] is a kind of special single hidden-layer feed-forward neural network (SLFN) in which the input-layer weights and hidden-layer biases are randomly selected and the ... See full document
18
Performance of Machine Learning for Lane Detection
... lane detection algorithms including this one may not create dependable outcomes if there is an uncommon change in brightening [14,15] in the picture which might be brought about in specific districts of the ... See full document
6
Taking North American White Supremacist Groups Seriously: The Scope and the Challenge of Hate Speech on the Internet
... contained hate-related content. Using a large-scale dataset and econometric techniques, they found a positive relationship between Internet penetration and offline racial hate ...online hate ... See full document
20
Detection of cognitive impairment using a machine-learning algorithm
... Patients and methods: The original dataset from the Clinical Research Center for Dementia of South Korea study was obtained. In total, 9,885 and 300 patients were randomly allocated to the training and test datasets, ... See full document
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