Abstract
Before we jump into creating bots and fancy natural language models, we will take a quick detour into natural language understanding (NLU) and some of its machine learning (ML) underpinnings. We will be implementing some of these NLU concepts using Microsoft’s Language Understanding Intelligence Service (LUIS) in the following chapter. Some other concepts are available for you to explore using other services (for instance, Microsoft’s Cognitive Services) or Python/R ML tools. This chapter is meant to equip you with a quick-and-dirty introduction into the ML space as it pertains to natural language tasks. If you are familiar with these concepts, by all means, skip ahead to Chapter 3. Otherwise, I hope to impart a base-level understanding of the roots of NLU and how it can be applied to the field of bots. There is a great plethora of content on the Internet that goes into depth about all of these topics; we provide the appropriate references if you feel adventurous!
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Notes
- 1.
Turing Test: https://en.wikipedia.org/wiki/Turing_test
- 2.
Ask Ray | Response to announcement of chat bot Eugene Goostman passing the Turing test: http://www.kurzweilai.net/ask-ray-response-to-announcement-of-chatbot-eugene-goostman-passing-the-turing-test
- 3.
- 4.
Elizabot: http://www.masswerk.at/elizabot/
- 5.
Machine Learning Explained: Understanding Supervised, Unsupervised, and Reinforcement Learning, Ronald Van Loon: https://www.datasciencecentral.com/profiles/blogs/machine-learning-explained-understanding-supervised-unsupervised
- 6.
Deep Reinforcement Learning Visualization: http://cs.stanford.edu/people/karpathy/convnetjs/demo/rldemo.html
- 7.
Convolutional neural networks (CNNs): http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/
- 8.
Recurrent neural networks (RNNs) and associated architectures: https://en.wikipedia.org/wiki/Recurrent_neural_network
- 9.
Comparative Study of CNN and RNN for Natural Language Processing: https://arxiv.org/pdf/1702.01923.pdf
- 10.
Microsoft Cognitive Toolkit: https://www.microsoft.com/en-us/cognitive-toolkit/
- 11.
TensorFlow: https://www.tensorflow.org/
- 12.
Microsoft researchers achieve new conversational speech recognition milestone: https://www.microsoft.com/en-us/research/blog/microsoft-researchers-achieve-new-conversational-speech-recognition-milestone
- 13.
A Neural Network for Machine Translation, at Production Scale - https://research.googleblog.com/2016/09/a-neural-network-for-machine.html
- 14.
Machine Learning, NLP: Text Classification using scikit-learn, python and NLTK: https://towardsdatascience.com/machine-learning-nlp-text-classification-using-scikit-learn-python-and-nltk-c52b92a7c73 a
- 15.
Evaluating Natural Language Understanding Service for Conversational Question Answering Systems: http://www.sigdial.org/workshops/conference18/proceedings/pdf/SIGDIAL22.pdf
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© 2018 Szymon Rozga
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Rozga, S. (2018). Chat Bot Natural Language Understanding. In: Practical Bot Development. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-3540-9_2
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DOI: https://doi.org/10.1007/978-1-4842-3540-9_2
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