Distributional Semantics

This book provides a comprehensive foundation for the use of distributional methods in computational modeling of meaning.

Alessandro Lenci (Author), Magnus Sahlgren (Author)

9781107004290, Cambridge University Press

Hardback, published 30 September 2023

452 pages
28 x 19 x 2.8 cm, 0.86 kg

'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

Distributional semantics develops theories and methods to represent the meaning of natural language expressions, with vectors encoding their statistical distribution in linguistic contexts. It is at once a theoretical model to express meaning, a practical methodology to construct semantic representations, a computational framework for acquiring meaning from language data, and a cognitive hypothesis about the role of language usage in shaping meaning. This book aims to build a common understanding of the theoretical and methodological foundations of distributional semantics. Beginning with its historical origins, the text exemplifies how the distributional approach is implemented in distributional semantic models. The main types of computational models, including modern deep learning ones, are described and evaluated, demonstrating how various types of semantic issues are addressed by those models. Open problems and challenges are also analyzed. Students and researchers in natural language processing, artificial intelligence, and cognitive science will appreciate this book.

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.

Subject Areas: Computational linguistics [CFX]