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)
11
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12
Index

Using weakly supervised labels to improve IMDb sentiment analysis

Sentiment analysis of movie reviews on the IMDb website is a standard task for classification-type Natural Language Processing (NLP) models. We used this data in Chapter 4 to demonstrate transfer learning with GloVe and VERT embeddings. The IMDb data set has 25,000 training examples and 25,000 testing examples. The dataset also includes 50,000 unlabeled reviews. In previous attempts, we ignored these unsupervised data points. Adding more training data will improve the accuracy of the model. However, hand labeling would be a time-consuming and expensive exercise. We'll use Snorkel-powered labeling functions to see if the accuracy of the predictions can be improved on the testing set.

Pre-processing the IMDb dataset

Previously, we used the tensorflow_datasets package to download and manage the dataset. However, we need lower-level access to the data to enable writing the labeling functions. Hence, the...