Jacob Eisenstein Reihenfolge der Bücher (Chronologisch)

This textbook surveys computational methods for understanding, generating, and manipulating human language, merging classical algorithms with modern machine learning techniques. It presents a technical perspective on natural language processing, focusing on data-driven approaches through supervised and unsupervised learning. The first section lays the groundwork in machine learning, providing tools for word-based textual analysis. The second section introduces structured language representations, such as sequences, trees, and graphs. The third section delves into linguistic meaning representation and analysis, covering formal logic to neural word embeddings. The final section discusses three key applications of natural language processing: information extraction, machine translation, and text generation, with end-of-chapter exercises that include both theoretical analysis and software implementation. The text synthesizes a wide range of research literature, connecting contemporary techniques with linguistic and computational foundations. It is designed for advanced undergraduate and graduate courses, as well as a reference for software engineers and data scientists. A background in computer programming and college-level mathematics is recommended. Mastery of the material equips students with the skills to develop and analyze innovative natural language processing systems and engage with current research in the field.