Alessandro Lenci, PhD, is Professor of Linguistics and Director of the Computational Linguistics Laboratory at the University of Pisa. His research interests include distributional semantics, computational linguistics, and natural language processing. The recipient of the '10-year Test-of-Time-Award' from the Association for Computational Linguistics in 2020, he has published extensively and coordinated many projects on natural language processing and cognitive science. Magnus Sahlgren, PhD, is Head of Research for Natural Language Understanding at AI Sweden. Known primarily for research on computational models of meaning, Sahlgren's work lies at the intersection of computational linguistics, machine learning, and artificial intelligence. His doctoral dissertation, entitled 'The Word-Space Model,' was awarded the prize for the 'Most Prominent Scholarly Achievement of 2006' by the Stockholm University Faculty of Humanities.

'Lenci and Sahlgren's textbook is a landmark contribution to the fast growing and increasingly important discipline of distributional semantics. They have managed to distill 60 years of diverse research on distributional semantics, from its beginning in structural and corpus linguistics and psychology, through the application of techniques from information retrieval and linear algebra, to the most recent developments driven by deep neural networks and large language models in NLP. The authors synthesize the major findings from different fields and integrate these diverse traditions into a comprehensive and coherent framework of distributional meaning. Lenci and Sahlgren's text promises to be the new standard for reference and teaching in this area.' James Pustejovsky, Brandeis University

• Preface; Part I. Theory: 1. From usage to meaning: the foundations of distributional semantics; 2. Distributional representations; Part II. Models: 3. Distributional semantic models; 4. Matrix models; 5. Random encoding models; Part III. Practice: 7. Evaluation of distributional semantic models; 8. Distributional semantics and the lexicon; 9 Distributional semantics beyond the lexicon; 10. Conclusions and Outlook; References; Index.

Alessandro Lenci, PhD, is Professor of Linguistics and Director of the Computational Linguistics Laboratory at the University of Pisa. His research interests include distributional semantics, computational linguistics, and natural language processing. The recipient of the '10-year Test-of-Time-Award' from the Association for Computational Linguistics in 2020, he has published extensively and coordinated many projects on natural language processing and cognitive science. Magnus Sahlgren, PhD, is Head of Research for Natural Language Understanding at AI Sweden. Known primarily for research on computational models of meaning, Sahlgren's work lies at the intersection of computational linguistics, machine learning, and artificial intelligence. His doctoral dissertation, entitled 'The Word-Space Model,' was awarded the prize for the 'Most Prominent Scholarly Achievement of 2006' by the Stockholm University Faculty of Humanities.

'Lenci and Sahlgren's textbook is a landmark contribution to the fast growing and increasingly important discipline of distributional semantics. They have managed to distill 60 years of diverse research on distributional semantics, from its beginning in structural and corpus linguistics and psychology, through the application of techniques from information retrieval and linear algebra, to the most recent developments driven by deep neural networks and large language models in NLP. The authors synthesize the major findings from different fields and integrate these diverse traditions into a comprehensive and coherent framework of distributional meaning. Lenci and Sahlgren's text promises to be the new standard for reference and teaching in this area.' James Pustejovsky, Brandeis University

• Preface; Part I. Theory: 1. From usage to meaning: the foundations of distributional semantics; 2. Distributional representations; Part II. Models: 3. Distributional semantic models; 4. Matrix models; 5. Random encoding models; Part III. Practice: 7. Evaluation of distributional semantic models; 8. Distributional semantics and the lexicon; 9 Distributional semantics beyond the lexicon; 10. Conclusions and Outlook; References; Index.

Alessandro Lenci, PhD, is Professor of Linguistics and Director of the Computational Linguistics Laboratory at the University of Pisa. His research interests include distributional semantics, computational linguistics, and natural language processing. The recipient of the '10-year Test-of-Time-Award' from the Association for Computational Linguistics in 2020, he has published extensively and coordinated many projects on natural language processing and cognitive science. Magnus Sahlgren, PhD, is Head of Research for Natural Language Understanding at AI Sweden. Known primarily for research on computational models of meaning, Sahlgren's work lies at the intersection of computational linguistics, machine learning, and artificial intelligence. His doctoral dissertation, entitled 'The Word-Space Model,' was awarded the prize for the 'Most Prominent Scholarly Achievement of 2006' by the Stockholm University Faculty of Humanities.

'Lenci and Sahlgren's textbook is a landmark contribution to the fast growing and increasingly important discipline of distributional semantics. They have managed to distill 60 years of diverse research on distributional semantics, from its beginning in structural and corpus linguistics and psychology, through the application of techniques from information retrieval and linear algebra, to the most recent developments driven by deep neural networks and large language models in NLP. The authors synthesize the major findings from different fields and integrate these diverse traditions into a comprehensive and coherent framework of distributional meaning. Lenci and Sahlgren's text promises to be the new standard for reference and teaching in this area.' James Pustejovsky, Brandeis University

• Preface; Part I. Theory: 1. From usage to meaning: the foundations of distributional semantics; 2. Distributional representations; Part II. Models: 3. Distributional semantic models; 4. Matrix models; 5. Random encoding models; Part III. Practice: 7. Evaluation of distributional semantic models; 8. Distributional semantics and the lexicon; 9 Distributional semantics beyond the lexicon; 10. Conclusions and Outlook; References; Index.