News Classification
27 papers with code • 3 benchmarks • 11 datasets
Benchmarks
These leaderboards are used to track progress in News Classification
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Datasets
IndicGLUE
N15News
CIDII Dataset
Most implemented papers
IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages
AI4Bharat/indic-bert
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Findings of the Association for Computational Linguistics 2020
These resources include: (a) large-scale sentence-level monolingual corpora, (b) pre-trained word embeddings, (c) pre-trained language models, and (d) multiple NLU evaluation datasets (IndicGLUE benchmark).
Graph Convolutional Network for Swahili News Classification
This work empirically demonstrates the ability of Text Graph Convolutional Network (Text GCN) to outperform traditional natural language processing benchmarks for the task of semi-supervised Swahili news classification.
Evaluating Various Tokenizers for Arabic Text Classification
However, given a large text corpus, representing all the words is not efficient in terms of vocabulary size.
Cross-lingual Evidence Improves Monolingual Fake News Detection
Misleading information spreads on the Internet at an incredible speed, which can lead to irreparable consequences in some cases.
N24News: A New Dataset for Multimodal News Classification
Current news datasets merely focus on text features on the news and rarely leverage the feature of images, excluding numerous essential features for news classification.
Transforming Fake News: Robust Generalisable News Classification Using Transformers
Experiments over the ISOT and Combined Corpus datasets show that transformers achieve an increase in F1 scores of up to 4. 9% for out of distribution generalisation compared to baseline approaches, with a further increase of 10. 1% following the implementation of our two-step classification pipeline.
Does Transliteration Help Multilingual Language Modeling?
We empirically measure the effect of transliteration on MLLMs in this context.
Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model
jinanzou/astock
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In addition, we propose a self-supervised learning strategy based on SRLP to enhance the out-of-distribution generalization performance of our system.
Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time
Temporal shifts -- distribution shifts arising from the passage of time -- often occur gradually and have the additional structure of timestamp metadata.
Multiverse: Multilingual Evidence for Fake News Detection
In this work, we propose Multiverse -- a new feature based on multilingual evidence that can be used for fake news detection and improve existing approaches.
