Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications, and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural lan...(展开全部)

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!

As online information grows dramatically, search engines such as Google are playing a more and more important role in our lives. Critical to all search engines is the problem of designing an effective retrieval model that can rank documents accurately for a given query. This has been a central research problem in information retrieval for several decades. In the past ten years,...(展开全部)

Ruth Garrett Millikan presents a highly original account of cognition - of how we get to grips with the world in thought. The question at the heart of her book is Kant's 'How is knowledge possible?', but answered from a contemporary naturalist standpoint. The starting assumption is that we are evolved creatures that use cognition as a guide in dealing with the natural world, an...(展开全部)

A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learning techniques. This textbook provides a technical perspective on natural language processing―methods for building computer software that understands, generates, and manipulates huma...(展开全部)

Multilingual Natural Language Processing Applications is the first comprehensive single-source guide to building robust and accurate multilingual NLP systems. Edited by two leading experts, it integrates cutting-edge advances with practical solutions drawn from extensive field experience. Part I introduces the core concepts and theoretical foundations of modern multilingual nat...(展开全部)

If you want to build, iterate and scale NLP systems in a business setting and to tailor them for various industry verticals, this is your guide. Consider the task of building a chatbot or text classification system at your organization. In the beginning, there may be little or no data to work with. At this point, a basic solution that uses rule based systems or traditional mach...(展开全部)

Daniel Jurafsky (http://www.stanford.edu/~jurafsky/) is an Associate Professor in the Department of Linguistics at Stanford University.