Cross-Disciplinary Advances in Applied Natural Language Processing
Issues and Approaches
Herausgeber: Boonthum-Denecke, Chutima; Lamkin, Travis; McCarthy, Philip M.
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Applied Natural Language Processing (ANLP) is interested in not only the creation of natural language processing approaches (i.e., tools, systems, algorithms, models, theories, and techniques), but it is also (and, arguably, more specifically) interested in how those approaches stack up against new problems, issues, identified knowledge gaps, or created data sets. Cross-Disciplinary Advances in Applied Natural Language Processing: Issues and Approaches defines the role of ANLP within NLP, and alongside other disciplines such as linguistics, computer science, and cognitive science. The descript...
Applied Natural Language Processing (ANLP) is interested in not only the creation of natural language processing approaches (i.e., tools, systems, algorithms, models, theories, and techniques), but it is also (and, arguably, more specifically) interested in how those approaches stack up against new problems, issues, identified knowledge gaps, or created data sets. Cross-Disciplinary Advances in Applied Natural Language Processing: Issues and Approaches defines the role of ANLP within NLP, and alongside other disciplines such as linguistics, computer science, and cognitive science. The description also includes the categorization of current ANLP research, and examples of current research in ANLP. This book is a useful reference for teachers, students, and materials developers in fields spanning linguistics, computer science, and cognitive science.
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