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Natural Language Processing with TensorFlow

You're reading from   Natural Language Processing with TensorFlow Teach language to machines using Python's deep learning library

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788478311
Length 472 pages
Edition 1st Edition
Languages
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Authors (2):
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Thushan Ganegedara Thushan Ganegedara
Author Profile Icon Thushan Ganegedara
Thushan Ganegedara
Motaz Saad Motaz Saad
Author Profile Icon Motaz Saad
Motaz Saad
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Table of Contents (14) Chapters Close

Preface 1. Introduction to Natural Language Processing 2. Understanding TensorFlow FREE CHAPTER 3. Word2vec – Learning Word Embeddings 4. Advanced Word2vec 5. Sentence Classification with Convolutional Neural Networks 6. Recurrent Neural Networks 7. Long Short-Term Memory Networks 8. Applications of LSTM – Generating Text 9. Applications of LSTM – Image Caption Generation 10. Sequence-to-Sequence Learning – Neural Machine Translation 11. Current Trends and the Future of Natural Language Processing A. Mathematical Foundations and Advanced TensorFlow Index

Newer machine learning models


Now we will discuss several newer machine learning models that have emerged to resolve various limitations of the current models (for example, standard LSTMs). One such model is Phased LSTMs that allow us to pay attention to very specific events that happen in future during learning. Another model is Dilated RNNs (DRNNs), which provides a way to model complex dependencies present in the inputs. DRNNs also enable parallel computation of unrolled RNNs, compared with naïvely iterating through the unrolled RNNs.

Phased LSTM

Current LSTM networks have shown a remarkable performance in many of the sequential learning tasks. However, they are not well-suited for processing irregularly timed data, such as data provided by event-driven sensors. This is mainly because no matter whether an event is transpired or not, an LSTM's cell state and the hidden states are continuously updated. This behavior can cause the LSTM to ignore special events that might rarely or irregularly...

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