Best Natural Language Processing Textbooks and Semantic Analysis
Natural Language Processing with Python will help you
- Extract information from unstructured text, either to guess the topic or identify "named entities"
- Analyze linguistic structure in text, including parsing and semantic analysis
- Access popular linguistic databases, including WordNet and treebanks
- Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence
An explosion of Web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology – at all levels and with all modern technologies – this text takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations.
The authors cover areas that traditionally are taught in different courses, to describe a unified vision of speech and language processing. Emphasis is on practical applications and scientific evaluation. An accompanying Website contains teaching materials for instructors, with pointers to language processing resources on the Web. The Second Edition offers a significant amount of new and extended material.
Natural Language Processing (NLP) is an important area of application development and its relevance in addressing contemporary problems will only increase in the future. There has been a significant increase in the demand for natural language-accessible applications supported by NLP tasks. Natural Language Processing with Java will explore how to automatically organize text using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization. It covers concepts of NLP that even those of you without a background in statistics or natural language processing can understand.
What You Will Learn
- Develop a deep understanding of the basic NLP tasks and how they relate to each other
- Discover and use the available tokenization engines
- Implement techniques for the end of sentence detection
- Apply search techniques to find people and things within a document
- Construct solutions to identify parts of speech within sentences
- Use parsers to extract relationships between elements of a document
- Integrate basic tasks to tackle more complex NLP problems
An explosion of web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology - at all levels and with all modern technologies - this book takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations.
Builds each chapter around one or more worked examples demonstrating the main idea of the chapter, using the examples to illustrate the relative strengths and weaknesses of various approaches. Adds coverage of statistical sequence labelling, information extraction, question answering and summarization, advanced topics in speech recognition, speech synthesis. Revises coverage of language modelling, formal grammars, statistical parsing, machine translation, and dialog processing. A useful reference for professionals in any of the areas of speech and language processing.
Many NLP tasks have at their core a subtask of extracting the dependencies—who did what to whom—from natural language sentences. This task can be understood as the inverse of the problem solved in different ways by diverse human languages, namely, how to indicate the relationship between different parts of a sentence.
Understanding how languages solve the problem can be extremely useful in both feature design and error analysis in the application of machine learning to NLP. Likewise, understanding cross-linguistic variation can be important for the design of MT systems and other multilingual applications.
The purpose of this book is to present in a succinct and accessible fashion information about the morphological and syntactic structure of human languages that can be useful in creating more linguistically sophisticated, more language-independent, and thus more successful NLP systems.
Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensemble of models for optimal accuracy of analysis and prediction. This book is one of a kind as it provides state of the art in advanced data analytics methods with important best practices to ensure the reader’s success in data analytics.
What You Will Learn
- More than 100 analytics techniques and methods are covered
- The book offers solutions and tips on handling missing data, noisy and dirty data
- How to error reduction and boosting signal to reduce noise
- Is ideal as a text book for a course or as a reference for data scientists, data engineers, data analysts, Business intelligence practitioner
- It finally introduces the underlying mathematical terms for those who want a mathematical foundation of the analytics methods
Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries.
The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.
This comprehensive reference work provides an overview of the concepts, methodologies, and applications in computational linguistics and natural language processing (NLP).
What You Will Learn
- Features contributions by the top researchers in the field, reflecting the work that is driving the discipline forward
- Includes an introduction to the major theoretical issues in these fields, as well as the central engineering applications that the work has produced
- Presents the major developments in an accessible way, explaining the close connection between scientific understanding of the computational properties of natural language and the creation of effective language technologies
- Serves as an invaluable state-of-the-art reference source for computational linguists and software engineers developing NLP applications in industrial research and development labs of software companies
From a leading authority in artificial intelligence, this book delivers a synthesis of the major modern techniques and the most current research in natural language processing. The approach is unique in its coverage of semantic interpretation and discourse alongside the foundational material in syntactic processing.
What You Will Learn
- How to parsing and make a grammar
- Ambiguity Resolution: Statistical Methods
- Semantics and Logical Form
- Linking Syntax and Semantics
- Ambiguity Resolution
- Linking Syntax and Semantics.