Do you know any Natural Language Processing best-sellers? Natural Language Processing (NLP) is a vast field and what is more important - today it's a fast-moving area of research. In this post we propose you to have a look at our review of the most interesting books about NLP. We know that every researcher and scientist must have a good theoretical foundation. That's why we are recommending these books for your consideration and discussion. 1. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition - it's a very useful book as for professionals so as for beginners in any of the areas of language and speech processing. The 2nd edition is now available here Free version of the 1st edition here2. Graph-based Natural Language Processing and Information Retrieval - in this book you will find a good description of the use of graph-based Natural Language Processing and Information Retrieval. It covers diverse topics such as:- lexical semantics- text summarization- text mining etc.3. Natural Language Processing for Online Applications: Text Retrieval, Extraction and CategorizationIn short this book is used to learn how your company can save cost by leveraging technologies from information retrieval, information extraction, and text categorization. 4. Foundations of Statistical Natural Language ProcessingThe book is covering the entire spectrum from parsing and disambiguation, sentence tagging, and machine translation, all the way to text analysis, information extraction, and document retrieval.Here You can read a remarkable review by Gerhard Weikum, University of the Saarland, Saarbrücken, GermanyThe book itself you can find hereWe also want to share with you good resources for learning programming languages and some programming techniques that can be applied for NLP.1. Manual for learning Haskell (a static, pure, lazy, functional language). Haskell was chosen as the main programming language for this book. Based on their own experience the authors noticed that very many natural language processing tasks are relatively straightforward data transformations. Haskell is a language that is exceptionally good at data transformations.2. The book for learning Python as the main programming language for NLP 3. In their book Jimmy Lin and Chris Dyer introduced the notion of MapReduce. The book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. Books provide us with general or specific topic related (like IR) view, so if you are looking deeply in the area and tasks it's better to remember about state-of-the-art achievements provided in research papers. You can start from Google publications of NLProc
