[PDF] Top 20 Abusive Language Detection with Graph Convolutional Networks
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Abusive Language Detection with Graph Convolutional Networks
... Despite the addition of improved author pro- files, several abusive tweets remain misclassified. As per our analysis, many of these tend to con- tain URL s to abusive content but not the content it- self, ... See full document
6
Detection of Autism using Magnetic Resonance Imaging data and Graph Convolutional Neural Networks
... 14 Adjacency matrices can also be filtered to create a node vector. The edge to node convolution operation is depicted in Figure 5 performs a filtering operation over all the neighboring edges of a single vertex and then ... See full document
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Graph convolutional networks for exploring authorship hypotheses
... There is a far richer space of traditional schol- arly hypotheses regarding the Bible that we plan to consider in future work. For example, the Deuteronomist sources are historically entangled with the historical books ... See full document
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Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks
... We apply GCNs to the semantic dependency graphs and experiment on the English–German language pair (WMT16). We observe an im- provement over the semantics-agnostic baseline (a BiRNN encoder; 23.3 vs 24.5 BLEU). As ... See full document
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Graph Convolutional Networks Meet Markov Random Fields: Semi-Supervised Community Detection in Attribute Networks
... munity detection in networks with ill-defined structures (He et ...one graph in its model, our network-specific MRF ...expected graph from a ran- dom-graph null model of the given ... See full document
8
Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media
... Our main insight in this paper is that the social context through which the information is propa- gated can be leveraged to alleviate the problem, by providing both a better representation for it, and when direct ... See full document
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Graph Convolutional Networks for Named Entity Recognition
... There is a large corpus of work on named entity recognition, with few studies using explicitly non-local information for the task. One early work by Finkel et al. (Finkel et al., 2005) uses Gibbs sampling to capture long ... See full document
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Question Answering by Reasoning Across Documents with Graph Convolutional Networks
... The long-standing goal of natural language under- standing is the development of systems which can acquire knowledge from text collections. Fresh in- terest in reading comprehension tasks was sparked by the ... See full document
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Attributed Graph Classification via Deep Graph Convolutional Neural Networks
... Graphs are a universal language for describing a set of complex systems (Zhang et al. 2018). There are complex systems all around us; society is a collection of over seven billion individuals, communication ... See full document
124
Semi supervised User Geolocation via Graph Convolutional Networks
... Social media user geolocation is vital to many applications such as event detection. In this paper, we propose GCN, a multiview geolocation model based on Graph Con- volutional Networks, that uses ... See full document
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Studying Generalisability across Abusive Language Detection Datasets
... the Abusive Language Detection space must be more representative of all facets of abusive language, if we expect them to generalise to any subset of ...neural networks (Lee et ... See full document
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Spam detection in im images using convolutional neural networks
... Tensor Flow: Tensor Flow is a system for Large-Scale Machine learning developed by the Google Brain team. Pioneered by Martin Abadi, and Paul Barham, it is a machine learning system that operates at large scale and in ... See full document
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Cross Domain Detection of Abusive Language Online
... tecting abusive language include the naive Bayes classifier (Kwok and Wang, 2013; Chen et ...neural networks (Gamb¨ack and Sikdar, 2017; Potapova and Gordeev, 2016; Pavlopoulos et ... See full document
6
Graph convolutional networks: a comprehensive review
... Many graph convolutional network models have been proposed, to name a few, including [5, 37, 42, 61, ...the graph-level (i.e., each document is modeled as a graph) and classify the texts by ... See full document
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Graph Convolutional Networks for Text Classification
... applied convolutional neural networks (convolu- tion on regular grid, ...flexible graph convolutional neural networks (convolution on non-grid, ...arbitrary graph) for the task. ... See full document
8
Geometric Hawkes Processes with Graph Convolutional Recurrent Neural Networks
... Recently, geometric deep learning becomes promising be- cause the convolutional framework can be applied on non- Euclidean data, e.g, graphs, to extract important features. Some studies such as (Niepert, Ahmed, ... See full document
8
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
... GCNs are neural networks operating on graphs and inducing features of nodes (i.e., real-valued vectors / embeddings) based on properties of their neighborhoods. In Kipf and Welling (2017), they were shown to be ... See full document
10
Spatial temporal graph convolutional networks for skeleton-based dynamic hand gesture recognition
... Hou et al. [12] designed an end-to-end spatial- temporal attention residual temporal convolutional net- work (STA-Res-TCN) which modifies temporal convo- lutional networks [13] for skeleton-based dynamic ... See full document
7
Racial Bias in Hate Speech and Abusive Language Detection Datasets
... Our study is the first to measure racial bias in hate speech and abusive language detection datasets. We find evidence of substantial racial bias in all of the datasets tested. This bias tends to ... See full document
11
Attention Guided Graph Convolutional Networks for Relation Extraction
... neural networks only on the shortest dependency path between the entities in the full ...apply graph convolu- tional networks (GCNs) (Kipf and Welling, 2017) model over a pruned ... See full document
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