Natural Language Processing Practical Approach
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Natural Language Processing: Practical Approach
Author: Syed Muzamil Basha
language: en
Publisher: MileStone Research Publications
Release Date: 2023-02-26
The "Natural Language Processing Practical Approach" is a textbook that provides a practical introduction to the field of Natural Language Processing (NLP). The goal of the textbook is to provide a hands-on, practical guide to NLP, with a focus on real-world applications and use cases. The textbook covers a range of NLP topics, including text preprocessing, sentiment analysis, named entity recognition, text classification, and more. The textbook emphasizes the use of algorithms and models to solve NLP problems and provides practical examples and code snippets in various programming languages, including Python. The textbook is designed for students, researchers, and practitioners in NLP who want to gain a deeper understanding of the field and build their own NLP projects. The current state of NLP is rapidly evolving with advancements in machine learning and deep learning techniques. The field has seen a significant increase in research and development efforts in recent years, leading to improved performance and new applications in areas such as sentiment analysis, text classification, language translation, and named entity recognition. The future prospects of NLP are bright, with continued development in areas such as reinforcement learning, transfer learning, and unsupervised learning, which are expected to further improve the performance of NLP models. Additionally, increasing amounts of text data available through the internet and growing demand for human-like conversational interfaces in areas such as customer service and virtual assistants will likely drive further advancements in NLP. The benefits of a hands-on, practical approach to natural language processing include: 1. Improved understanding: Practical approaches allow students to experience the concepts and techniques in action, helping them to better understand how NLP works. 2. Increased motivation: Hands-on approaches to learning can increase student engagement and motivation, making the learning process more enjoyable and effective. 3. Hands-on experience: By working with real data and implementing NLP techniques, students gain hands-on experience in applying NLP techniques to real-world problems. 4. Improved problem-solving skills: Practical approaches help students to develop problem-solving skills by working through real-world problems and challenges. 5. Better retention: When students have hands-on experience with NLP techniques, they are more likely to retain the information and be able to apply it in the future. A comprehensive understanding of NLP would include knowledge of its various tasks, techniques, algorithms, challenges, and applications. It also involves understanding the basics of computational linguistics, natural language understanding, and text representation methods such as tokenization, stemming, and lemmatization. Moreover, hands-on experience with NLP tools and libraries like NLTK, Spacy, and PyTorch would also enhance one's understanding of NLP.
Natural Language Processing and Computational Linguistics
Author: Bhargav Srinivasa-Desikan
language: en
Publisher: Packt Publishing Ltd
Release Date: 2018-06-29
Work with Python and powerful open source tools such as Gensim and spaCy to perform modern text analysis, natural language processing, and computational linguistics algorithms. Key Features Discover the open source Python text analysis ecosystem, using spaCy, Gensim, scikit-learn, and Keras Hands-on text analysis with Python, featuring natural language processing and computational linguistics algorithms Learn deep learning techniques for text analysis Book Description Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data. This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy. You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning. This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis. What you will learn Why text analysis is important in our modern age Understand NLP terminology and get to know the Python tools and datasets Learn how to pre-process and clean textual data Convert textual data into vector space representations Using spaCy to process text Train your own NLP models for computational linguistics Use statistical learning and Topic Modeling algorithms for text, using Gensim and scikit-learn Employ deep learning techniques for text analysis using Keras Who this book is for This book is for you if you want to dive in, hands-first, into the interesting world of text analysis and NLP, and you're ready to work with the rich Python ecosystem of tools and datasets waiting for you!
Handbook on Natural Language Processing for Requirements Engineering
This handbook provides a comprehensive guide on how natural language processing (NLP) can be leveraged to enhance various aspects of requirements engineering (RE), leading the reader from the exploration of fundamental concepts and techniques to the practical implementation of NLP for RE solutions in real-world scenarios. The book features contributions from researchers with both academic and industrial experience. It is organized into three parts, each focusing on different aspects of applying NLP to RE: Part I – NLP for Downstream RE Tasks delves into the application of NLP techniques to tasks that are typically part of the RE process. It includes chapters on NLP for requirements classification, requirements similarity and retrieval, requirements traceability, defect detection, and automated terminology and relations extraction. Next, Part II – NLP for Specialised Types of Requirements and Artefacts explores how NLP can be tailored to handle specific requirement types and artefacts. The chapters cover legal requirements processing, privacy requirements acquisition and analysis, user feedback intelligence, mining issue trackers, and analysis of user story requirements. Eventually, Part III – NLP for RE in Practice addresses practical applications and tools for implementing NLP in RE. It includes a chapter on the different tools that use NLP techniques for RE tasks, followed by chapters on empirical evaluation of tools, practical guidelines for selecting and evaluating NLP techniques, guidelines on using large language models (LLMs) in RE, and dealing with data challenges in RE. The book is designed for a diverse audience, including Ph.D. students, researchers, and practitioners. Ph.D. students can benefit from a comprehensive guide to the topic of NLP for RE and acquire the essential background for their studies. Researchers can identify further triggers for scientific exploration, based on the currently settled knowledge in the field. Eventually, practitioners facing challenges with NL requirements can find practical insights to enhance their RE processes using NLP.