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Related Concept Videos

Language01:16

Language

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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
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The self is a central aspect of human identity, encompassing an individual’s beliefs, emotions, perceptions, and experiences. It is a cognitive and psychological construct that enables individuals to interpret their traits and behaviors, influencing how they perceive themselves and interact with the world. While personality consists of stable and enduring characteristics, the self is shaped by self-perception and social experiences. This distinction highlights the dynamic nature of the...
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Components of Language01:24

Components of Language

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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Language Development01:22

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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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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.
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The self-concept encompasses individuals' beliefs about themselves, structured through cognitive frameworks known as self-schemas. These schemas function as mental representations of specific traits or behaviors, influencing how self-relevant information is perceived, processed, and remembered. For example, individuals who are schematic for body weight are more likely to interpret routine experiences—such as dining out or shopping—through the lens of that trait. Conversely, those...
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Incorporating Demographic Embeddings Into Language Understanding.

Justin Garten1,2, Brendan Kennedy1,2, Joe Hoover2,3

  • 1Department of Computer Science, University of Southern California.

Cognitive Science
|January 17, 2019
PubMed
Summary

This study introduces situated demographic embeddings, a novel method that combines participant demographics with contextual information. This approach enhances natural language understanding models, particularly in low-resource scenarios, by improving performance and addressing data sparsity.

Keywords:
Continuous representationsDemographic representationMoral reasoningNatural language processingNeural networks

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Meaning is highly dependent on context, including participant identity and background.
  • Demographic factors influence interpretation but their relevance varies across situations.
  • Existing models struggle to capture the dynamic interplay between context and demographics.

Purpose of the Study:

  • To develop a method for integrating demographic information with contextual data for improved natural language understanding.
  • To create situated demographic embeddings that represent participants in a continuous geometric space tailored to specific domains.
  • To enhance the performance of natural language understanding tasks, especially in data-scarce environments.

Main Methods:

  • Developed a novel approach for combining demographic factors and contextual information into situated demographic embeddings.
  • Mapped representations into a continuous geometric space suitable for specific domains.
  • Utilized external data to apply the method in low-resource situations.

Main Results:

  • Demonstrated that situated demographic embeddings are functional and interpretable.
  • Showcased the effectiveness of the approach in improving natural language understanding model performance.
  • Successfully addressed issues related to data sparsity in modeling.

Conclusions:

  • Situated demographic embeddings offer a powerful way to model context-dependent meaning.
  • The method enhances the robustness and accuracy of natural language understanding systems.
  • This approach holds significant potential for advancing AI in diverse real-world applications.