Abstract
Natural language processing (NLP) is an interdisciplinary subfield, which has components from major fields such as linguistics, computer science, and artificial intelligence. NLP is mainly concerned with the interaction between humans and computers. The scope of NLP ranges from computational understanding and generation of human languages to processing and analyzing large amounts of natural language data. The scope of NLP also contains text, speech, cognition, and their interactions. In this chapter, we briefly cover the history of NLP, the differences between rule-based NLP and statistical NLP, and the major NLP methods and techniques. We finally conduct a case study on NLP to prepare you for real-world problems.
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Notes
- 1.
The Unreasonable Effectiveness of Recurrent Neural Networks, available on http://karpathy.github.io/2015/05/21/rnn-effectiveness
- 2.
Text generation with an RNN | TensorFlow Core TensorFlow, available on www.tensorflow.org/tutorials/text/text_generation
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© 2021 Orhan Gazi Yalçın
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Yalçın, O.G. (2021). Natural Language Processing. In: Applied Neural Networks with TensorFlow 2. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-6513-0_9
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DOI: https://doi.org/10.1007/978-1-4842-6513-0_9
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Publisher Name: Apress, Berkeley, CA
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