Neural Network Methods for Natural Language Processing

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Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.

Original languageEnglish
Pages (from-to)1-311
Number of pages311
JournalSynthesis Lectures on Human Language Technologies
Issue number1
StatePublished - 2017


  • deep learning
  • machine learning
  • natural language processing
  • neural networks
  • recurrent neural networks
  • sequence to sequence models
  • supervised learning
  • word embeddings

All Science Journal Classification (ASJC) codes

  • Linguistics and Language
  • Computer Science Applications
  • Computer Networks and Communications


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