"Words are not just labels for conceptual categories. Words construct conceptual categories, frame situations and influence behavior. Where do they get their meaning? This book describes how words acquire their meaning. The author argues that mechanisms based on associations, pattern detection, and feature matching processes explain how words acquire their meaning from experience and from language alike. Such mechanisms are summarized by the distributional hypothesis, a computational theory of meaning originally applied to word occurrences only, and hereby extended to extra-linguistic contexts. By arguing in favor of the cognitive foundations of the distributional hypothesis, which suggests that words that appear in similar contexts have similar meaning, this book offers a theoretical account for word meaning construction and extension in first and second language that bridges empirical findings from cognitive and computer sciences. Plain language and illustrations accompany the text, making this book accessible to a multidisciplinary academic audience"--.

• Where Words Get their Meaning
• Editorial page
• Title page
• Copyright page
• Dedication page
• Table of contents
• Acknowledgements
• 1. Word power
  • 1.1 Introduction
  • 1.2 Outline of the book
  • 1.3 What this book is about and what it leaves out
  • 1.4 A final remark on the parallel between human and artificial mind
• Part 1. Word meaning construction and representation in the human mind
• 2. Word meaning mental representation
  • 2.1 Learning words: A developmental perspective
  • 2.2 Cross-situational learning
  • 2.3 Words denoting abstract vs. concrete concepts
  • 2.4 How words construct meaning
  • 2.5 Summary
• 3. Word meaning extension: Deriving new meanings from old ones
  • 3.1 Word meaning representation and conceptual representation
  • 3.2 Meaning extension by polysemy
  • 3.3 Meaning extension by metonymy
  • 3.4 Meaning extension by metaphor
  • 3.5 Summary
• 4. The bilingual mind and the bilingual mental lexicon
  • 4.1 Theoretical models of the bilingual mental lexicon
  • 4.2 Word associations in native speakers and language learners
  • 4.3 Incidental vocabulary leaning
  • 4.4 Statistical learning based on crossing linguistic contexts and crossing situations
  • 4.5 Pattern detection: A hallmark of human cognition
    • World-to-world associations
    • Word-to-world associations
    • Word-to-word associations
  • 4.6 Summary
• Part 2 .Word meaning construction and representation in the artificial mind
• 5. Distributional models and word embeddings
  • 5.1 You shall know a word by the company it keeps
  • 5.2 Constructing distributional models
  • 5.3 Macro types of distributional models
    • 5.3.1 Structured and unstructured models
    • 5.3.2 Explicit and implicit vectors
  • 5.4 From frequency-based models to word embeddings
  • 5.5 Summary
• 6. Evaluating distributional models
  • 6.1 Evaluating distributional models against psychological data
  • 6.2 Learning associations by conditioning
  • 6.3 Associative and discriminative learning
  • 6.4 Grounded and ungrounded symbols
  • 6.5 Word meaning in native speakers, language learners, and distributional models
  • 6.6 Summary
• 7. Distributional models beyond language
  • 7.1 Word meaning is both, embodied and symbolic
  • 7.2 Multimodal representation of word meaning
  • 7.3 Flickr Distributional Tagspace, a distributional model based on annotated images
  • 7.4 From word-to-world to world-to-world modelling
  • 7.5 Summary
• Part 3. Converging evidence in language and communication research
• 8. Where words get their meaning
  • 8.1 How language and experience construct categories
  • 8.2 Word-to-world associations in constructing the meaning of words denoting concrete and abstract concepts
  • 8.3 Word-to-word associations in constructing the meaning of words denoting concrete and abstract concepts
  • 8.4 Word meaning organization in the L1 and L2
  • 8.5 Summary
• 9. The cognitive foundations of the distributional hypothesis
  • 9.1 Leaving the Chinese room and climbing the ladder of abstraction
  • 9.2 The distributional hypothesis applied to metaphor
  • 9.3 The distributional hypothesis applied to metonymy
  • 9.4 The power of language as a driving force to abstraction
  • 9.5 Summary
• 10. Conclusions and outlook
  • 10.1 AI behaviorism: Learning how the mind constructs word meaning by looking at how machines do it
  • 10.2 Practical implications for the study of human creativity
  • 10.3 Practical implications for the study of first language acquisition
  • 10.4 Practical implications for learning and teaching a foreign language
  • 10.5 Outlook
• References
• Index