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Neural Graph Embedding Methods for Natural Language Processing
[article]
2020
arXiv
pre-print
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Recently, Graph Convolutional Networks (GCNs) have been proposed to address this shortcoming and have been successfully applied for several problems. ...
In the second part of the thesis, we utilize GCNs for Document Timestamping problem and for learning word embeddings using dependency context of a word instead of sequential context. ...
Convolution based framework for incorporating diverse semantic information in learned embeddings. ...
arXiv:1911.03042v3
fatcat:fruw547yxnev5pmnlij76wovcy
Visualising Argumentation Graphs with Graph Embeddings and t-SNE
[article]
2021
arXiv
pre-print
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It shows that such a visualisation approach can work for argumentation and show interesting structural properties of argumentation graphs, opening up paths for further research in the area. ...
This paper applies t-SNE, a visualisation technique familiar from Deep Neural Network research to argumentation graphs by applying it to the output of graph embeddings generated using several different ...
After the model had been trained for 4 hours, the training was stopped and the output of the last convolutional layer was used as a graph embedding for visualisation by extracting the raw features of the ...
arXiv:2107.00528v1
fatcat:wrq7ftvefbdofmfqg6eyzmdgrm
Semi-Supervised Node Classification by Graph Convolutional Networks and Extracted Side Information
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2009.13734v1 fatcat:c7c5hqti45hqpcbevq4ewnpb3a
First, this paper provides a method for extracting side information from a graph realization. ...
Indeed, the extracted side information can be replaced by a sequence of side information that is independent of the graph structure. ...
Extracting Side Information For extracting side information that is as much as possible independent from the output of the GCN block, the side information is extracted either from the given feature matrix ...
arXiv:2009.13734v2
fatcat:tqk3kcb2abhc3igulyavifxuvi
HybridTabNet: Towards Better Table Detection in Scanned Document Images
2021
Applied Sciences
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Tables in document images are an important entity since they contain crucial information. Therefore, accurate table detection can significantly improve the information extraction from documents. ...
Our two-stage table detector uses the ResNeXt-101 backbone for feature extraction and Hybrid Task Cascade (HTC) to localize the tables in scanned document images. ...
We used ResNeXt-101 as a backbone for feature extraction with Cardinality = 64 and bottleneck width = 4d. ...
doi:10.3390/app11188396
fatcat:uwzdjpyfmnbnxbxzamgbqkvrji
EX-Action: Automatically Extracting Threat Actions from Cyber Threat Intelligence Report Based on Multimodal Learning
2021
Security and Communication Networks
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To address this problem, we propose EX-Action, a framework for extracting threat actions from CTI reports. ...
At the same time, a metric is used to evaluate the information completeness of the extracted action obtained by EX-Action. ...
Action Feature Extraction. In this module, EX-Action extracts five types of features for each action. e extraction framework of action's features is shown in Figure 3 . ...
doi:10.1155/2021/5586335
fatcat:f56ms2kidfcrbmxio3lvyw7oie
Predictive Modeling Applied to Structured Clinical Data Extracted from Electronic Health Records: An Architectural Hypothesis and A First Experiment
2021
Journal of Computer Science
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We try the architecture by slightly increasing both cardinalities of datasets and extracted features. ...
Second module produces predictions and it implements alternatively one from Graph Convolutional Network, Simplified Graph Transduction Game, Nearest Nodes and Classes Graph. ...
Then, second module (based on Graph Convolutional Network (GCN), Simplified Graph Transduction Game (SGTG) or Nearest Nodes and Classes Graph (NNCG)) makes a prediction on a particular patient. ...
doi:10.3844/jcssp.2021.762.775
fatcat:da2fhk54wjcfxaynwr6fnadqtm
Semi-supervised learning of hierarchical representations of molecules using neural message passing
[article]
2017
arXiv
pre-print
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In this paper, we propose an unsupervised hierarchical feature extraction algorithm for molecules (or more generally, graph-structured objects with fixed number of types of nodes and edges), which is applicable ...
With the rapid increase of compound databases available in medicinal and material science, there is a growing need for learning representations of molecules in a semi-supervised manner. ...
Graph convolutions, which are extensions of convolution operation from multi-dimensional arrays like images or texts to arbitrary graphs, are attracting much attention. ...
arXiv:1711.10168v2
fatcat:g2nk7v7lgjcpppjpr2as5yzhti
A Survey of Deep Learning Approaches for OCR and Document Understanding
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2011.13534v1 fatcat:jkokxo63xrac7dfdqm6cqctrp4
In this survey paper, we review different techniques for document understanding for documents written in English and consolidate methodologies present in literature to act as a jumping-off point for researchers ...
Documents are a core part of many businesses in many fields such as law, finance, and technology among others. ...
., 2011)
Information Extraction The goal of information extraction for document understanding is to take documents that may have diverse layouts and extract information into a structured format. ...
arXiv:2011.13534v2
fatcat:4d3htgyqyjbh7loxrovy7fe5w4
Graph-Based Siamese Network for Authorship Verification
2022
Mathematics
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We model the documents in a graph representation and then a graph neural network extracts relevant features from these graph representations. ...
We propose a Siamese Network architecture composed of graph convolutional networks along with pooling and classification layers. ...
Acknowledgments: The authors thank CONACYT for the computer resources provided through the INAOE Supercomputing Laboratory's Deep Learning Platform for Language Technologies. ...
doi:10.3390/math10020277
fatcat:b45k7hhb6fgjpioku74d6tnrai
Confidence-based Graph Convolutional Networks for Semi-Supervised Learning
[article]
2019
arXiv
pre-print
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Recently, Graph Convolutional Networks (GCNs) have achieved impressive performance on the graph-based SSL task. ...
Graph-based Semi-Supervised Learning (SSL) methods aim to address this problem by labeling a small subset of the nodes as seeds and then utilizing the graph structure to predict label scores for the rest ...
al., 2015) is a generalization of RNN framework which can be used for graph-structured data. • GPNN (Liao et al., 2018 ) is a graph partition based algorithm which propagates information after partitioning ...
arXiv:1901.08255v2
fatcat:mhtsuthbybhwzpllgtajvdjkcq
Robust Spatial Filtering With Graph Convolutional Neural Networks
2017
IEEE Journal on Selected Topics in Signal Processing
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2017 pre-print arXiv:1703.00792v2 fatcat:yuyvafk6lnbrxdsmp3epyglldaOther Versions
As opposed to spectral methods, our framework, which we term Graph-CNNs, defines filters as polynomials of functions of the graph adjacency matrix. ...
The simplicity and elegance of the convolutional filtering process makes them perfect for structured problems such as image, video, or voice, where vertices are homogeneous in the sense of number, location ...
DeepWalk [26] learns latent representations of graph vertices by using random walks to extract local information which encodes structural regularities in social networks. ...
doi:10.1109/jstsp.2017.2726981
fatcat:sj2bq77u2faateg3nnnl75sewu
Convolutional Neural Networks Via Node-Varying Graph Filters
[article]
2018
arXiv
pre-print
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The proposed design replaces the classical convolution not with a node-invariant graph filter (GF), which is the natural generalization of convolution to graph domains, but with a node-varying GF. ...
This filter extracts different local features without increasing the output dimension of each layer and, as a result, bypasses the need for a pooling stage while involving only local operations. ...
This is the length for which, when using two layers, the information corresponding to the whole graph can be aggregated. ...
arXiv:1710.10355v2
fatcat:jkk3h2zap5apnlus4vsiv6rbyu
Towards Complex Product Line Variability Modelling: Mining Relationships from Non-Boolean Descriptions
2019
Journal of Systems and Software
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This approach is based on Formal Concept Analysis and Pattern Structures, two mathematical frameworks for knowledge discovery that bring theoretical foundations to complex variability extraction algorithms ...
In this paper, we propose an approach to extract complex variability information, i.e., involving features as well as multi-valued attributes and cardinalities, in the form of logical relationships. ...
Our approach offers a mathematical framework for complex variability information extraction based on a unique and canonical structure. ...
doi:10.1016/j.jss.2019.06.002
fatcat:m272yunvpja6vot32oy5u73hoq
2020 Index IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 42
2021
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Zhang, R., +, TPAMI Feb. 2020 291-303
Document image processing
Baselines Extraction from Curved Document Images via Slope Fields
Recovery. ...
., +, TPAMI June 2020 1289-1302 Baselines Extraction from Curved Document Images via Slope Fields Recovery. ...
Matsukawa, T., +, 2179 -2194 Learning with Privileged Information via Adversarial Discriminative Modality Distillation. ...
doi:10.1109/tpami.2020.3036557
fatcat:3j6s2l53x5eqxnlsptsgbjeebe
Linked Document Embedding for Classification
2016
Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM '16
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In this paper, we study the problem of linked document embedding for classification and propose a linked document embedding framework LDE, which combines link and label information with content information ...
to learn document representations for classification. ...
and label information into a probabilistic framework for classification. • CNN [11] : convolution neural network for classification. ...
doi:10.1145/2983323.2983755
dblp:conf/cikm/WangTAL16
fatcat:bfgay4jan5ealg5czt5hpkxdxi
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