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Related Concept Videos

Language and Cognition01:27

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
Language Development01:22

Language Development

Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Components of Language01:24

Components of Language

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Chunking01:12

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Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology
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Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Published on: June 29, 2021

A Bayesian framework for word segmentation: exploring the effects of context.

Sharon Goldwater1, Thomas L Griffiths, Mark Johnson

  • 1School of Informatics, University of Edinburgh, Informatics Forum, 10 Crichton Street, Edinburgh EH89AB, UK. sgoldwat@inf.ed.ac.uk

Cognition
|May 5, 2009
PubMed
Summary
This summary is machine-generated.

Statistical learning helps infants segment words. Assuming words predict each other, not just independence, leads to more accurate word segmentation in speech, crucial for early language development.

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Area of Science:

  • Cognitive Science
  • Developmental Psychology
  • Computational Linguistics

Background:

  • Infants learn language through statistical regularities in speech.
  • Understanding word segmentation is key to early language acquisition.

Purpose of the Study:

  • To computationally model how infants segment words from speech.
  • To investigate the impact of different learner assumptions on word segmentation accuracy.

Main Methods:

  • Developed Bayesian ideal observer models.
  • Analyzed segmented words from child-directed speech corpora.
  • Compared models assuming word independence versus word predictiveness.

Main Results:

  • The independence assumption led to undersegmentation, misidentifying multi-word phrases as single words.
  • Assuming words are predictive resulted in significantly more accurate word segmentation.
  • Contextual information is vital for successful statistical word segmentation.

Conclusions:

  • Learners benefit from assuming words are predictive units, not just independent ones.
  • Infants may utilize sophisticated statistical patterns for word segmentation.
  • Contextual awareness is crucial for effective statistical word segmentation strategies.