This textbook provides a technical perspective on natural language processing―methods for building computer software that understands, generates, and manipulates human language.
It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. The sections does one or more of the following;
- establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis.
- introduces structured representations of language, including sequences, trees, and graphs.
- explores different approaches to the representation and analysis of linguistic meaning, ranging from formal logic to neural word embeddings.
- offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation.
- End-of-chapter exercises include both paper-and-pencil analysis and software implementation.
After mastering the material presented, students will have the technical skill to build and analyze novel natural language processing systems and to understand the latest research in the field. |
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