Hands-On Natural Language Processing with Python

Preface

Before the advent of deep learning, traditional natural language processing (NLP) approaches had been widely used in tasks such as spam filtering, sentiment classification, and part of speech (POS) tagging. These classic approaches utilized statistical characteristics of sequences such as word count and co-occurrence, as well as simple linguistic features. However, the main disadvantage of these techniques was that they could not capture complex linguistic characteristics, such as context and intra-word dependencies.

Recent developments in neural networks and deep learning have given us powerful new tools to match human-level performance on NLP tasks and build products that deal with natural language. Deep learning for NLP is centered ...