Book Image

Advanced Natural Language Processing with TensorFlow 2

By : Ashish Bansal, Tony Mullen
Book Image

Advanced Natural Language Processing with TensorFlow 2

By: Ashish Bansal, Tony Mullen

Overview of this book

Recently, there have been tremendous advances in NLP, and we are now moving from research labs into practical applications. This book comes with a perfect blend of both the theoretical and practical aspects of trending and complex NLP techniques. The book is focused on innovative applications in the field of NLP, language generation, and dialogue systems. It helps you apply the concepts of pre-processing text using techniques such as tokenization, parts of speech tagging, and lemmatization using popular libraries such as Stanford NLP and SpaCy. You will build Named Entity Recognition (NER) from scratch using Conditional Random Fields and Viterbi Decoding on top of RNNs. The book covers key emerging areas such as generating text for use in sentence completion and text summarization, bridging images and text by generating captions for images, and managing dialogue aspects of chatbots. You will learn how to apply transfer learning and fine-tuning using TensorFlow 2. Further, it covers practical techniques that can simplify the labelling of textual data. The book also has a working code that is adaptable to your use cases for each tech piece. By the end of the book, you will have an advanced knowledge of the tools, techniques and deep learning architecture used to solve complex NLP problems.
Table of Contents (13 chapters)
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Loading the data

Data can be downloaded from the University of Groningen website as follows:

# alternate: download the file from the browser and put # in the same directory as this notebook

Please note that the data is quite large – over 800MB. If wget is not available on your system, you may use any other tool such as, curl or a browser to download the data set. This step may take some time to complete. If you have a challenge accessing the data set from the University server, you may download a copy from Kaggle: Also note that since we are going to be working on large data sets, some of the following steps may take some time to execute. In the world of Natural Language Processing (NLP), more training data and training time is key to great results.

All the code for this example can be found in the NER with BiLSTM and CRF.ipynb notebook...