Multilingual Natural Language Processing Applications

From Theory to Practice

Part I introduces the core concepts and theoretical foundations of modern multilingual natural language processing, presenting today’s best practices for understanding word and document structure, analyzing syntax, modeling language, recognizing entailment, and detecting redundancy.

Part II thoroughly addresses the practical considerations associated with building real-world applications, including information extraction, machine translation, information retrieval/search, summarization, question answering, distillation, processing pipelines, and more.

This book contains important new contributions from leading researchers at IBM, Google, Microsoft, Thomson Reuters, BBN, CMU, University of Edinburgh, University of Washington, University of North Texas, and others.

Coverage includes

Core NLP problems, and today’s best algorithms for attacking them

• Processing the diverse morphologies present in the world’s languages
• Uncovering syntactical structure, parsing semantics, using semantic role labeling, and scoring grammaticality
• Recognizing inferences, subjectivity, and opinion polarity
• Managing key algorithmic and design tradeoffs in real-world applications
• Extracting information via mention detection, coreference resolution, and events
• Building large-scale systems for machine translation, information retrieval, and summarization
• Answering complex questions through distillation and other advanced techniques
• Creating dialog systems that leverage advances in speech recognition, synthesis, and dialog management
• Constructing common infrastructure for multiple multilingual text processing applications

This book will be invaluable for all engineers, software developers, researchers, and graduate students who want to process large quantities of text in multiple languages, in any environment: government, corporate, or academic.