Automatic textual Knowledge Extraction based on Paragraph Constitutive Relations

Peng, Zuquan; Song, Huazhu; Kang, Baihan; Oloulade, Babatounde Moctard; He, Miao; Zheng, Xiaohan

doi:10.1109/icsai48974.2019.9010349

Cited by 2 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning (DL) models have achieved superior performance in fields such as computer vision [1,2] , natural language processing [3][4][5] , and speech recognition [6,7] because of their ability to capture hidden patterns and leverage the statistical properties of data. DL models, e.g., recurrent neural networks (RNNs) and convolutional neural networks (CNNs), have been widely used by researchers for machine learning (ML) tasks and have solved a wide variety of problems in many cross disciplines, including but not limited to medicine [8,9] , agriculture [10] , commerce [11,12] , and finance [13] .…”

Section: Introductionmentioning

confidence: 99%

Graph neural architecture search: A survey

Oloulade

Gao

Chen

et al. 2022

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

In academia and industries, graph neural networks (GNNs) have emerged as a powerful approach to graph data processing ranging from node classification and link prediction tasks to graph clustering tasks. GNN models are usually handcrafted. However, building handcrafted GNN models is difficult and requires expert experience because GNN model components are complex and sensitive to variations. The complexity of GNN model components has brought significant challenges to the existing efficiencies of GNNs. Hence, many studies have focused on building automated machine learning frameworks to search for the best GNN models for targeted tasks. In this work, we provide a comprehensive review of automatic GNN model building frameworks to summarize the status of the field to facilitate future progress. We categorize the components of automatic GNN model building frameworks into three dimensions according to the challenges of building them. After reviewing the representative works for each dimension, we discuss promising future research directions in this rapidly growing field.

show abstract

Section: Introductionmentioning

confidence: 99%

Graph neural architecture search: A survey

Oloulade

Gao

Chen

et al. 2022

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…However, deep learning models face the difficulty of extracting more comprehensive sentimental and emotional features since a large amount of emotional information is not utilized. As a result, more researchers try to integrate emotional information [12] and language knowledge [13] into the models [14][15][16]. Despite the great success of these models, they face the problem of extracting more comprehensive text emotional features since such models heavily rely on emotional resources and text information.…”

Section: Introductionmentioning

confidence: 99%

Transformer-Based Graph Convolutional Network for Sentiment Analysis

et al. 2022

Self Cite

View full text Add to dashboard Cite

Sentiment Analysis is an essential research topic in the field of natural language processing (NLP) and has attracted the attention of many researchers in the last few years. Recently, deep neural network (DNN) models have been used for sentiment analysis tasks, achieving promising results. Although these models can analyze sequences of arbitrary length, utilizing them in the feature extraction layer of a DNN increases the dimensionality of the feature space. More recently, graph neural networks (GNNs) have achieved a promising performance in different NLP tasks. However, previous models cannot be transferred to a large corpus and neglect the heterogeneity of textual graphs. To overcome these difficulties, we propose a new Transformer-based graph convolutional network for heterogeneous graphs called Sentiment Transformer Graph Convolutional Network (ST-GCN). To the best of our knowledge, this is the first study to model the sentiment corpus as a heterogeneous graph and learn document and word embeddings using the proposed sentiment graph transformer neural network. In addition, our model offers an easy mechanism to fuse node positional information for graph datasets using Laplacian eigenvectors. Extensive experiments on four standard datasets show that our model outperforms the existing state-of-the-art models.

show abstract

Automatic textual Knowledge Extraction based on Paragraph Constitutive Relations

Cited by 2 publications

References 6 publications

Graph neural architecture search: A survey

Graph neural architecture search: A survey

Transformer-Based Graph Convolutional Network for Sentiment Analysis

Contact Info

Product

Resources

About