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Text classification aims to effectively categorize documents into pre-defined categories.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, and Quoc V. Le. 2019 · 1906
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Newsweeder: Learning to filter netnews
Ken Lang. 1995 · 1995
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Snowball: Extracting relations from large plain-text collections
Eugene Agichtein and Luis Gravano. 2000 · 2000
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Computing semantic relatedness using wikipedia-based explicit semantic analysis
Evgeniy Gabrilovich and Shaul Markovitch. 2007 · 2007
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Using “annotator rationales” to improve machine learning for text categorization
Omar Zaidan, Jason Eisner, and Christine Piatko. 2007 · 2007
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Importance of semantic representation: Dataless classification
Ming Wei Chang, Lev Ratinov, Dan Roth, and Vivek Srikumar. 2008 · 2008
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Polynomial calculation of the shapley value based on sampling
Javier Castro, Daniel Gómez, and Juan Tejada. 2009 · 2009
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Text classification using label names only: A language model self-training approach
Yu Meng, Yunyi Zhang, Jiaxin Huang, Chenyan Xiong, Heng Ji, Chao Zhang, and Jiawei Han. 2020 · 2010
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Comprehensible classification models: a position paper
Alex A Freitas. 2014 · 2014
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Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014 · 2014
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On dataless hierarchical text classification
Yangqiu Song and Dan Roth. 2014 · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller. 2014 · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus. 2014 · 2014
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Document modeling with gated recurrent neural network for sentiment classification
Duyu Tang, Bing Qin, and Ting Liu. 2015 · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015 · 2015
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News Aggregator
Fabio Gasparetti. 2016 · 2016
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Investigating the influence of noise and distractors on the interpretation of neural networks
Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, and Sven Dähne. 2016 · 2016
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Doc2cube: Allocating documents to text cube without labeled data
Fangbo Tao, Chao Zhang, Xiusi Chen, Meng Jiang, Tim Hanratty, Lance Kaplan, and Jiawei Han. 2018 · 2018
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Publicly available clinical bert embeddings
Emily Alsentzer, John Murphy, William Boag, Wei-Hung Weng, Di Jindi, Tristan Naumann, and Matthew McDermott. 2019 · 2019
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Data programming for learning discourse structure
Sonia Badene, Kate Thompson, Jean-Pierre Lorré, and Nicholas Asher. 2019 · 2019
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A diagnostic study of explainability techniques for text classification
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein. 2020a · 2020
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A diagnostic study of explainability techniques for text classification
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein. 2020b · 2020
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Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Model-agnostic interpretability of machine learning
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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Hierarchical attention networks for document classification
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016 · 2016
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A unified view of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, A. Cengiz Öztireli, and Markus H. Gross. 2017 · 2017
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Data programming: Creating large training sets, quickly
Alexander Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, and Christopher Ré. 2017 · 2017
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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The mimic code repository: enabling reproducibility in critical care research
Alistair EW Johnson, David J Stone, Leo A Celi, and Tom J Pollard. 2018 · 2018
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In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages 323–333, Online. Association for Computational Linguistics
Dheeraj Mekala and Jingbo Shang. 2020 · 2020
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Learning with weak supervision for email intent detection
Kai Shu, Subhabrata Mukherjee, Guoqing Zheng, Ahmed Hassan Awadallah, Milad Shokouhi, and Susan Dumais. 2020 · 2020
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X-class: Text classification with extremely weak supervision
Zihan Wang, Dheeraj Mekala, and Jingbo Shang. 2021 · 2021
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Weakly-supervised text classification based on keyword graph
Lu Zhang, Jiandong Ding, Yi Xu, Yingyao Liu, and Shuigeng Zhou. 2021 · 2021
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
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Weakly supervised text classification using supervision signals from a language model
Ziqian Zeng, Weimin Ni, Tianqing Fang, Xiang Li, Xinran Zhao, and Yangqiu Song. 2022 · 2022
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Unsupervised key event detection from massive text corpora
Yunyi Zhang, Fang Guo, Jiaming Shen, and Jiawei Han. 2022 · 2022
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