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Text anomaly detection is crucial for identifying spam, misinformation, and offensive language in natural language processing tasks.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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Albert: A lite bert for self-supervised learning of language representations
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Term-weighting approaches in automatic text retrieval
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Lof: identifying density-based local outliers
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Efficient algorithms for mining outliers from large data sets
Sridhar Ramaswamy, Rajeev Rastogi, and Kyuseok Shim. 2000 · 2000
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Estimating the support of a high-dimensional distribution
Bernhard Schölkopf, John C Platt, John Shawe-Taylor, Alex J Smola, and Robert C Williamson. 2001 · 2001
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Spam filtering with naive bayes-which naive bayes?
Vangelis Metsis, Ion Androutsopoulos, and Georgios Paliouras. 2006 · 2006
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Isolation forest
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. 2008 · 2008
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Contributions to the study of sms spam filtering: new collection and results
Tiago A Almeida, José María G Hidalgo, and Akebo Yamakami. 2011 · 2011
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Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm
Markus Goldstein and Andreas Dengel. 2012 · 2012
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Isolation-based anomaly detection
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. 2012 · 2012
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Efficient estimation of word representations in vector space
Tomas Mikolov. 2013 · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
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A review of novelty detection
Marco AF Pimentel, David A Clifton, Lei Clifton, and Lionel Tarassenko. 2014 · 2014
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Automated hate speech detection and the problem of offensive language
Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017 · 2017
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Isolation-based anomaly detection using nearest-neighbor ensembles
Tharindu R Bandaragoda, Kai Ming Ting, David Albrecht, Fei Tony Liu, Ye Zhu, and Jonathan R Wells. 2018 · 2018
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Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Deep one-class classification
Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Predicting the type and target of offensive posts in social media
Lunar: Unifying local outlier detection methods via graph neural networks
Adam Goodge, Bryan Hooi, See-Kiong Ng, and Wee Siong Ng. 2022 · 2022
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Ecod: Unsupervised outlier detection using empirical cumulative distribution functions
Zheng Li, Yue Zhao, Xiyang Hu, Nicola Botta, Cezar Ionescu, and George H Chen. 2022 · 2022
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Ad-nlp: A benchmark for anomaly detection in natural language processing
Matei Bejan, Andrei Manolache, and Marius Popescu. 2023 · 2023
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Text classification using embeddings: a survey
Liliane Soares da Costa, Italo L Oliveira, and Renato Fileto. 2023 · 2023
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Comparative analysis of anomaly detection algorithms in text data
Yizhou Xu, Jérôme Milleret, and Frédérique Segond. 2023 · 2023
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Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, and Ritesh Kumar. 2019b · 2019
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Pyod: A python toolbox for scalable outlier detection
Yue Zhao, Zain Nasrullah, and Zheng Li. 2019 · 2019
Cited alongside, same era.
Copod: copula-based outlier detection
Zheng Li, Yue Zhao, Nicola Botta, Cezar Ionescu, and Xiyang Hu. 2020 · 2020
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Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers
Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. 2020 · 2020
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A review on outlier/anomaly detection in time series data
Ane Blázquez-García, Angel Conde, Usue Mori, and Jose A Lozano. 2021 · 2021
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A heuristic-driven ensemble framework for covid-19 fake news detection
Sourya Dipta Das, Ayan Basak, and Saikat Dutta. 2021 · 2021
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Deep learning for anomaly detection: A review
Guansong Pang, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel. 2021 · 2021
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Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Haonan Chen, Zheng Liu, Zhicheng Dou, and Ji-Rong Wen. 2023 · 2023
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Nlp-adbench: Nlp anomaly detection benchmark
Yuangang Li, Jiaqi Li, Zhuo Xiao, Tiankai Yang, Yi Nian, Xiyang Hu, and Yue Zhao. 2024 · 2024
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New embedding models and api updates
OpenAI. 2024 · 2024
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Deep graph anomaly detection: A survey and new perspectives
Hezhe Qiao, Hanghang Tong, Bo An, Irwin King, Charu Aggarwal, and Guansong Pang. 2024 · 2024
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Qwen2.5: A party of foundation models
Qwen Team. 2024 · 2024
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Log2graphs: An unsupervised framework for log anomaly detection with efficient feature extraction
Caihong Wang, Du Xu, and Zonghang Li. 2024 · 2024
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An enhanced fake news detection system with fuzzy deep learning
Cheng Xu and M-Tahar Kechadi. 2024 · 2024
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Jasper and stella: distillation of sota embedding models
Dun Zhang, Jiacheng Li, Ziyang Zeng, and Fulong Wang. 2024 · 2024
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