natural-language-processing-11

News Classification

3260 papers • 126 benchmarks • 313 datasets

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N15News

Soham News Article Classification

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Most implemented papers

Explainable Tsetlin Machine Framework for Fake News Detection with Credibility Score Assessment

Ole-Christoffer Granmo, Bimal Bhattarai, Lei Jiao•Tue May 18 2021

A novel interpretable fake news detection framework based on the recently introduced Tsetlin Machine is proposed, utilizing the conjunctive clauses of the TM to capture lexical and semantic properties of both true and fake news text.

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Content

BBC Hindi News Article Classification

N15News

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AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset

Mohamed Seghir Hadj Ameur, H. Aliane•Thu May 06 2021

This paper releases "AraCOVID19-MFH" a manually annotated multi-label Arabic COVID-19 fake news and hate speech detection dataset that contains 10,828 Arabic tweets annotated with 10 different labels.

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Paper Graph

Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles

Boshko Koloski, Timen Stepisnik Perdih, Marko Robnik-Sikonja, S. Pollak, Blaž Škrlj•Tue Oct 19 2021

To the authors' knowledge this is the first largerscale evaluation of how knowledge graph-based representations can be systematically incorporated into the process of fake news classification, and it is demonstrated that knowledge graphs already achieve competitive performance to conventionally accepted representation learners.

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A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification

Dairui Liu, Derek Greene, Ruihai Dong•Sun Mar 13 2022

A novel practical framework is proposed by utilizing a two-tier attention architecture to decouple the complexity of explanation and the decision-making process and is applied in the context of a news article classification task.

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Accuracy of TextFooler black box adversarial attacks on 01 loss sign activation neural network ensemble

Yunzhe Xue, Usman Roshan•Sun Feb 11 2024

The work here suggests that 01 loss sign activation networks could be further developed to create fool proof models against text adversarial attacks.

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Speed Reading: Learning to Read ForBackward via Shuttle

Tsu-Jui Fu, Wei-Yun Ma•Sun Dec 31 2017

This work presents LSTM-Shuttle, which applies human speed reading techniques to natural language processing tasks for accurate and efficient comprehension and shows that it predicts both better and more quickly.

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Fake News Detection as Natural Language Inference

Hung-Yu kao, Kai-Chou Yang, Timothy Niven•Sun Feb 10 2019

This report describes the entry by the Intelligent Knowledge Management (IKM) Lab in the WSDM 2019 Fake News Classification challenge, treating the task as natural language inference (NLI), and individually train a number of the strongest NLI models as well as BERT.

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Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification

E. Hovy, Vaibhav Vaibhav, Raghuram Mandyam Annasamy•Sat Oct 26 2019

An interesting pattern in the way sentences interact with each other across different kind of news articles is observed, and a graph neural network-based model is proposed which does away with the need of feature engineering for fine grained fake news classification.

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Assessing Robustness of Text Classification through Maximal Safe Radius Computation

Emanuele La Malfa, A. Hartshorn, Min Wu, L. Laurenti, Benjie Wang, M. Kwiatkowska•Wed Sep 30 2020

This paper focuses on robustness of text classification against word substitutions, aiming to provide guarantees that the model prediction does not change if a word is replaced with a plausible alternative, such as a synonym.

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KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi

Andre Niyongabo Rubungo, Hong Qu, Julia Kreutzer, Li Huang•Thu Oct 22 2020

The experiments show that training embeddings on the relatively higher-resourced Kinyarwanda yields successful cross-lingual transfer to Kirundi, and the design of the created datasets allows for a wider use in NLP beyond text classification in future studies, such as representation learning, cross-lingsual learning with more distant languages, or as base for new annotations for tasks such as parsing, POS tagging, and NER.

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