Natural Language Processing Recipes Unlocking Text Data with Machine Learning and Deep Learning using Python /

• Κύριοι συγγραφείς: Kulkarni, Akshay (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Shivananda, Adarsha (http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berkeley, CA : Apress : Imprint: Apress, 2019.
• Έκδοση: 1st ed. 2019.
• Θέματα: Artificial intelligence.; Python (Computer program language).; Open source software.; Computer programming.; Artificial Intelligence.; Python.; Open Source.

 Implement natural language processing applications with Python using a problem-solution approach. This book has numerous coding exercises that will help you to quickly deploy natural language processing techniques, such as text classification, parts of speech identification, topic modeling, text summarization, and sentiment analysis. Natural Language Processing Recipes starts by offering solutions for cleaning and preprocessing text data and ways to analyze it with advanced algorithms. You'll see practical applications of the semantic as well as syntactic analysis of text, as well as complex natural language processing approaches that involve text normalization, advanced preprocessing, POS tagging, parsing, text summarization, and sentiment analysis. You will also learn various applications of machine learning and deep learning in natural language processing. By using the recipes in this book, you will have a toolbox of solutions to apply to your own projects in the real world, making your development time quicker and more efficient. You will: Apply NLP techniques using Python libraries such as NLTK, TextBlob, spaCy, Stanford CoreNLP, and many more Implement the concepts of information retrieval, text summarization, sentiment analysis, and other advanced natural language processing techniques. Identify machine learning and deep learning techniques for natural language processing and natural language generation problems. 

 Chapter 1: Extracting the data -- Chapter 2: Exploring and processing text data -- Chapter 3: Converting text to features -- Chapter 4: Advanced natural language processing -- Chapter 5: Implementing Industry Applications -- Chapter 6: Deep learning for NLP.