ADVANCED NATURAL LANGUAGE PROCESSING WITH TENSORFLOW 2 Build Real-world Effective Nlp... Applications Using Ner, Rnns, Seq2seq Models, Tran

This book is focused on cutting-edge applications in the fields of NLP, language generation, and dialog systems. It covers the concepts of pre-processing text using techniques such as tokenization, parts-of-speech (POS) tagging, and lemmatization using popular libraries such as Stanford NLP and spaCy. Named Entity Recognition (NER) models are built from scratch using Bi-directional Long Short-Term Memory networks (BiLSTMs), Conditional Random Fields (CRFs), and Viterbi decoding. Taking a very practical, application-focused perspective, the book covers key emerging areas such as generating text for use in sentence completion and text summarization, multi-modal networks that bridge images and text by generating captions for images, and managing the dialog aspects of chatbots. It covers one of the most important reasons behind recent advances of NLP – transfer learning and fine tuning. Unlabeled textual data is easily available but labeling this data is costly