ME-GCN: Multi-dimensional Edge-Embedded Graph Convolutional Networks for Semi-supervised Text Classification

Wang, Kunze; Han, Soyeon Caren; Long, Siqu; Poon, Josiah

doi:10.48550/arxiv.2204.04618

Cited by 4 publications

(5 citation statements)

References 20 publications

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“…TextGCN [5] adopts the GCN framework to learn representations for both words and documents without the use of external embeddings. Recent studies have improved upon this to learn multiple types of information through multi-edge [7] and multi-aspect [6] graphs. It has since been applied to long and short document classification [28,29], aspect-based sentiment analysis (ABSA) [30][31][32], and general text classification tasks [5,33].…”

Section: Graph Convolutional Networkmentioning

confidence: 99%

“…To represent emotions and their correlation with the text, we can consider two types of textual representation techniques: sequential text representation and graph-based text representation. While sequential text representation promotes capturing text features from local consecutive word sequences, graph-based text representation can attract widespread attention and successfully understand word and document relationships [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MM-EMOG: Multi-Label Emotion Graph Representation for Mental Health Classification on Social Media

Cabral,

Han,

Poon

et al. 2024

Robotics

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More than 80% of people who commit suicide disclose their intention to do so on social media. The main information we can use in social media is user-generated posts, since personal information is not always available. Identifying all possible emotions in a single textual post is crucial to detecting the user’s mental state; however, human emotions are very complex, and a single text instance likely expresses multiple emotions. This paper proposes a new multi-label emotion graph representation for social media post-based mental health classification. We first construct a word–document graph tensor to describe emotion-based contextual representation using emotion lexicons. Then, it is trained by multi-label emotions and conducts a graph propagation for harmonising heterogeneous emotional information, and is applied to a textual graph mental health classification. We perform extensive experiments on three publicly available social media mental health classification datasets, and the results show clear improvements.

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Section: Graph Convolutional Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MM-EMOG: Multi-Label Emotion Graph Representation for Mental Health Classification on Social Media

Cabral,

Han,

Poon

et al. 2024

Robotics

View full text Add to dashboard Cite

show abstract

“…Furthermore, unlike voxel graph CNNs and EventConv [57,60], SplineConv can be run asynchronously using the method proposed in [58]. SplineConvs are also more expressive than GCNs which can only use one-dimensional features [61,62]. In the case of geometric graphs, this feature is usually taken as the distance between nodes.…”

Section: Challenges With Event-based Vision and Existing Solutionsmentioning

confidence: 99%

TactiGraph: An Asynchronous Graph Neural Network for Contact Angle Prediction Using Neuromorphic Vision-Based Tactile Sensing

Sajwani¹,

Ayyad²,

Alkendi³

et al. 2023

Preprint

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Vision-based tactile sensors (VBTS) have become the de facto method of giving robots the ability to obtain tactile feedback from their environment. Unlike other solutions to tactile sensing, VBTS offers high spatial resolution feedback without compromising on instrumentation costs or incurring additional maintenance expenses. However, conventional cameras used in VBTS have a fixed update rate and output redundant data, leading to computational overhead downstream. In this work, we present a neuromorphic vision-based tactile sensor (N-VBTS) that employs observations from an event-based camera for contact angle prediction. Particularly, we design and develop a novel graph neural network, dubbed TactiGraph, that asynchronously operates on graphs constructed from raw N-VBTS streams exploiting their spatiotemporal correlations to perform predictions. Although conventional VBTS uses an internal illumination source, TactiGraph is reported to perform efficiently in both scenarios, with and without an internal illumination source. Rigorous experimental results revealed that TactiGraph achieved a mean absolute error of 0.62∘ in predicting the contact angle and was faster and more efficient than both conventional VBTS and other N-VBTS, with lower instrumentation costs. Specifically, N-VBTS requires only 5.5% of the compute-time needed by VBTS when both are tested on the same scenario.

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“…However, this learning model obtained very low accuracy. Wang et al (2022) introduced a multidimensional edge-enhanced graph convolutional networks (ME-GCN) for the text classification in a semi-supervised manner. To effectively handle the closeness or distance between words and documents as multidimensional edge features, a multidimensional edge feature trained on the corpus is proposed.…”

Section: 4mentioning

confidence: 99%

ACOVMD: Automatic COVID‐19 misinformation detection in Twitter using self‐trained semi‐supervised hybrid deep learning model

Birunda,

Devi,

Muthukannan

et al. 2023

Int Social Sci J

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During the COVID‐19 pandemic, online social networks are extensively utilized, more than ever before by 8.4%, resulting in the propagation of false information related to COVID‐19. Despite the existence of many fake news detection models; annotation inconsistency, memory consumption, accurate and self‐trained efficient algorithms for detecting the emerging COVID‐19 misinformation tweets are still challenging. Hence, the main aim of this work is to come up with a self‐trained semi‐supervised model that accurately and automatically detects the reliability of emerging COVID‐19 tweets without delay. In this work, COVID‐19 tweet dataset is created in English Language from the period January 2020 to January 2022 as a ground truth database. Then self‐trained semi‐supervised hybrid deep learning model is proposed to train both supervised and unsupervised components simultaneously using the created dataset. The proposed model is self‐trained repeatedly and the model gets updated to predict the reliability of upcoming COVID‐19 tweets that differ from training tweets. We performed experiments multiple times by limiting the percentage amount of labelled tweets shown to the model, namely 80%, 50%, 40%, 30%, 20% and 10% labelled tweets, respectively. Experimental results show that the proposed model achieves 80.92% accuracy and 98.15% accuracy in the 10% and 80% label‐seen experiments, respectively. This shows a clear rising trend in the performance curve. Therefore, this technique will be useful for effectively classifying voluminous amounts of emerging tweets generated as part of the COVID‐19 infodemic. The proposed model may efficiently use a huge amount of unlabelled tweets and enhance the model's generalization performance.

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ME-GCN: Multi-dimensional Edge-Embedded Graph Convolutional Networks for Semi-supervised Text Classification

Cited by 4 publications

References 20 publications

MM-EMOG: Multi-Label Emotion Graph Representation for Mental Health Classification on Social Media

MM-EMOG: Multi-Label Emotion Graph Representation for Mental Health Classification on Social Media

TactiGraph: An Asynchronous Graph Neural Network for Contact Angle Prediction Using Neuromorphic Vision-Based Tactile Sensing

ACOVMD: Automatic COVID‐19 misinformation detection in Twitter using self‐trained semi‐supervised hybrid deep learning model

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