The following external documentation provides additional information:
- Coughlan, S. 2011. Spelling mistakes 'cost millions' in lost online sales.
- Bernoff, J. 2016. Bad Writing Costs Businesses Billions.
- Subramaniam, L. Venkata, et al. 2009. A Survey of Types of Text Noise and Techniques to Handle Noisy Text - Proceedings of The Third Workshop on Analytics for Noisy Unstructured Text Data.
- Apostolova, Emilia, and R. Andrew Kreek. 2018. Training and Prediction Data Discrepancies: Challenges of Text Classification with Noisy, Historical Data.
- Jianqiang, Zhao and Gui Xiaolin. 2017. Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis.
- Camacho-Collados, Jose, and Mohammad Taher Plehvar. 2017. On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis.
- Eler, Danilo Medeiros, et al. 2018. Analysis of Document Preprocessing Effects in Text and Opinion Mining.
- Banushka, Aliaksandr, and Petr, Hajek. 2019. The Effect of Text Preprocessing Strategies on Detecting Fake Consumer Reviews.
- Haddi, Emma, Xiaohui Liu, and Yong Shi. 2013. The Role of Text Preprocessing in Sentiment Analysis.
- Etoori Pravallika, Manoj Chinnakotla, and Radhika Mamidi. 2018. Automatic Spelling Correction for Resource-scarce Languages Using Deep Learning.
- Huang, Yinghao, Yi Lu Murphey and Yao Ge. 2013. Automotive Diagnosis Typo Correction Using Domain Knowledge and Machine Learning.
- Freitas, Diamantino, et al. 2002. A Project of Speech Input and Output in an E-commerce Application.” International Conference for Natural Language Processing in Portugal.
- Xie, Xia, et al. 2020. A Novel Text Mining Approach for Scholar Information Extraction from Web Content in Chinese.
- Zaki, Taher, Mohamed Salin EL Bazzi, and Driss Mammass. 2018. An Evolutionary Model for Selecting Relevant Textual Features.
- Galitsky, Boris. 2019. Chatbot Components and Architectures.