Book Image

Natural Language Processing with TensorFlow

By : Motaz Saad, Thushan Ganegedara
Book Image

Natural Language Processing with TensorFlow

By: Motaz Saad, Thushan Ganegedara

Overview of this book

Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks. Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator. After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks.
Table of Contents (16 chapters)
Natural Language Processing with TensorFlow

The BLEU score – evaluating the machine translation systems

BLEU stands for Bilingual Evaluation Understudy and is a way of automatically evaluating machine translation systems. This metric was first introduced in the paper, BLEU: A Method for Automatic Evaluation of Machine Translation, Papineni and others, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, July 2002: 311-318. We will be implementing the BLEU score calculation algorithm and is available as an exercise in bleu_score_example.ipynb. Let's understand how this is calculated.

Let's consider an example to learn the calculations of the BLEU score. Say, we have two candidate sentences (that is, a sentence predicted by our MT system) and a reference sentence (that is, corresponding actual translation) for some given source sentence:

  • Reference 1: The cat sat on the mat

  • Candidate 1: The cat is on the mat

To see how good the translation is, we can use one measure, precision. Precision...