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Deep Active Learning for Text Classification

Authors:

Huimin HanAuthors Info & Claims

ICVISP 2018: Proceedings of the 2nd International Conference on Vision, Image and Signal Processing

Article No.: 22, Pages 1 - 6

https://doi.org/10.1145/3271553.3271578

Published: 27 August 2018 Publication History

Abstract

In recent years, Active Learning (AL) has been applied in the domain of text classification successfully. However, traditional methods need researchers to pay attention to feature extraction of datasets and different features will influence the final accuracy seriously. In this paper, we propose a new method that uses Recurrent Neutral Network (RNN) as the acquisition function in Active Learning called Deep Active Learning (DAL). For DAL, there is no need to consider how to extract features because RNN can use its internal state to process sequences of inputs. We have proved that DAL can achieve the accuracy that cannot be reached by traditional Active Learning methods when dealing with text classification. What's more, DAL can decrease the need of the great number of labeled instances for Deep Learning (DL).

At the same time, we design a strategy to distribute label work to different workers. We have proved by using a proper batch size of instance, we can save much time but not decrease the model's accuracy. Based on this, we provide batch of instances for different workers and the size of batch is determined by worker's ability and scale of dataset, meanwhile, it can be updated with the performance of the workers.

References

[1]

Aggarwal, C. C., and Zhai, C. 2012. A survey of text classification algorithms. In Mining text data. Springer. 163--222.

[2]

Chen, Y. and Krause, A. 2013. Near-optimal batch mode active learning and adaptive submodular optimization. In ICML'13 Proceedings of the 30th International Conference on International Conference on Machine Learning.

Digital Library

[3]

Gal, Y., Islam, R. and Ghahramani, Z. 2016. Deep Bayesian Active Learning with Image Data. In: Advances in Neural Information Processing Systems 29.

[4]

Gal, Y. 2016. Uncertainty in Deep Learning. Doctor. University of Cambridge.

[5]

Hassan, S., Rafi, M. and Shaikh, M. 2011. Comparing SVM and naïve Bayes classifiers for text categorization with Wikitology as knowledge enrichment. In IEEE 14th International Multitopic Conference.

[6]

Hoi, S., Jin R., Zhu J. and Lyu M. 2006. Batch Mode Active Learning and Its Application to Medical Image Classification. In ICML '06 Proceedings of the 23rd international conference on Machine learning.

Digital Library

[7]

Huang, K. and Lin, H. 2016. A Novel Uncertainty Sampling Algorithm for Cost-sensitive Multiclass Active Learning. In 2016 IEEE 16th International Conference on Data Mining (ICDM).

[8]

Kim, Y. 2014. Convolutional Neural Networks for Sentence Classification. In Empirical Methods in Natural Language Processing.

[9]

Lai, S., Xu, L., Liu, K. and Zhao, J. 2015. Recurrent Convolutional Neural Networks for Text Classification. In Twenty-Ninth AAAI Conference on Artificial Intelligence

Digital Library

[10]

Lilleberg, J., Zhu, Y. and Zhang, Y. 2015. Support Vector Machines and Word2vec for Text Classification with Semantic Features. In IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing.

[11]

Rajaraman, A., Ullman, J.D. 2011. "Data Mining". Mining of Massive Datasets. pp. 1--17. ISBN 978-1-139-05845-2.

Digital Library

[12]

Ramos, J.E. 2003. Using TF-IDF to Determine Word Relevance in Document Queries.

[13]

Settles, B. 2010. Active Learning Literature Survey.

Digital Library

[14]

Settles, B. and Craven, M. 2008. An Analysis of Active Learning Strategies for Sequence Labeling Tasks. In Empirical Methods in Natural Language Processing.

Digital Library

[15]

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R. 2014. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15 (1929--1958).

Digital Library

[16]

Sum, M., Li, J., Guo, Z., Zhao, Y., Zheng, Y., Si, X. and Liu, Z. 2016. THUCTC: An Efficient Chinese Text Classifier.

[17]

Thompson, C., Califf, M. and Mooney, R. 1999. Active Learning for Natural Language Parsing and Information Extraction. In Proceedings of the Sixteenth International Machine Learning Conference.

Digital Library

[18]

Tong, S. and Koller, D. 2001. Support Vector Machine Active Learning with Applications to Text Classification. Machine Learning Research.

Digital Library

[19]

Wang, X., Jiang, W. and Luo, Z. 2016. Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts. Osaka, Japan, pp.2428--2437.

[20]

Yin, W., Kann, K., Yu, M. and Schütze, H. 2017. Comparative Study of CNN and RNN for Natural Language Processing.

Cited By

Saputra MSetiawan E(2023)Aspect Based Sentiment Analysis Using Recurrent Neural Networks (RNN) on Social Media Twitter2023 International Conference on Data Science and Its Applications (ICoDSA)10.1109/ICoDSA58501.2023.10276768(265-270)Online publication date: 9-Aug-2023
https://doi.org/10.1109/ICoDSA58501.2023.10276768
Ofori-Boateng RAceves-Martins MJayne CWiratunga NMoreno-Garcia C(2023)Evaluation of Attention-Based LSTM and Bi-LSTM Networks For Abstract Text Classification in Systematic Literature Review AutomationProcedia Computer Science10.1016/j.procs.2023.08.149222:C(114-126)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.procs.2023.08.149
Li WLi BNiu SWang ZLiu BNiu T(2023)Selecting informative data for defect segmentation from imbalanced datasets via active learningAdvanced Engineering Informatics10.1016/j.aei.2023.10193356(101933)Online publication date: Apr-2023
https://doi.org/10.1016/j.aei.2023.101933
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Index Terms

Deep Active Learning for Text Classification
1. Computing methodologies
  1. Machine learning
    1. Learning settings
      1. Active learning settings

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cover image ACM Other conferences

ICVISP 2018: Proceedings of the 2nd International Conference on Vision, Image and Signal Processing

August 2018

402 pages

ISBN:9781450365291

DOI:10.1145/3271553

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 27 August 2018

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ICVISP 2018

ICVISP 2018: The 2nd International Conference on Vision, Image and Signal Processing

August 27 - 29, 2018

NV, Las Vegas, USA

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Overall Acceptance Rate 186 of 424 submissions, 44%

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Cited By

Saputra MSetiawan E(2023)Aspect Based Sentiment Analysis Using Recurrent Neural Networks (RNN) on Social Media Twitter2023 International Conference on Data Science and Its Applications (ICoDSA)10.1109/ICoDSA58501.2023.10276768(265-270)Online publication date: 9-Aug-2023
https://doi.org/10.1109/ICoDSA58501.2023.10276768
Ofori-Boateng RAceves-Martins MJayne CWiratunga NMoreno-Garcia C(2023)Evaluation of Attention-Based LSTM and Bi-LSTM Networks For Abstract Text Classification in Systematic Literature Review AutomationProcedia Computer Science10.1016/j.procs.2023.08.149222:C(114-126)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.procs.2023.08.149
Li WLi BNiu SWang ZLiu BNiu T(2023)Selecting informative data for defect segmentation from imbalanced datasets via active learningAdvanced Engineering Informatics10.1016/j.aei.2023.10193356(101933)Online publication date: Apr-2023
https://doi.org/10.1016/j.aei.2023.101933
Yamashita YIto HWakabayashi KKobayashi MMorishima ABaeza-Yates RWeller KPortela MSeneviratne OWeber IYasseri TBon ASrinivas SIbáñez L(2022)HAEM: Obtaining Higher-Quality Classification Task Results with AI WorkersProceedings of the 14th ACM Web Science Conference 202210.1145/3501247.3531580(118-128)Online publication date: 26-Jun-2022
https://dl.acm.org/doi/10.1145/3501247.3531580
Li RYamakata YTajima K(2022)Active Learning Strategies Based on Text Informativeness2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00015(32-39)Online publication date: Nov-2022
https://doi.org/10.1109/WI-IAT55865.2022.00015
Rashid MJamour FHristidis VDemartini GZuccon GCulpepper JHuang ZTong H(2021)QuAXProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482289(1518-1527)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482289
Flores CFigueroa RPezoa J(2021)Active Learning for Biomedical Text Classification Based on Automatically Generated Regular ExpressionsIEEE Access10.1109/ACCESS.2021.30640009(38767-38777)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3064000
Li ZGurgel HDessay NHu LXu LGong P(2020)Semi-Supervised Text Classification Framework: An Overview of Dengue Landscape Factors and Satellite Earth ObservationInternational Journal of Environmental Research and Public Health10.3390/ijerph1712450917:12(4509)Online publication date: 23-Jun-2020
https://doi.org/10.3390/ijerph17124509
Prova AAkter TIslam MUddin MHossain THannan MHossain M(2019)Analysis of Online Marketplace Data on Social Networks Using LSTM2019 5th International Conference on Advances in Electrical Engineering (ICAEE)10.1109/ICAEE48663.2019.8975591(381-385)Online publication date: Sep-2019
https://doi.org/10.1109/ICAEE48663.2019.8975591

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