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Components of Language01:24

Components of Language

509
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.
509
Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

1.8K
Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
1.8K
Language and Cognition01:27

Language and Cognition

504
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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Language Development01:22

Language Development

529
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.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
529
Language01:16

Language

453
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...
453
State Space Representation01:27

State Space Representation

325
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
325

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Updated: Oct 12, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Language Representation Models: An Overview.

Thorben Schomacker1, Marina Tropmann-Frick1

  • 1Department of Computer Science, Hamburg University of Applied Sciences, 20099 Hamburg, Germany.

Entropy (Basel, Switzerland)
|November 27, 2021
PubMed
Summary

Recent advances in neural networks and transfer learning have significantly improved natural language processing (NLP). These techniques now outperform human baselines in general language understanding evaluations.

Keywords:
attention-based modelsdeep learningembeddingsmulti-task learningnatural language processingneural networkstransformer

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Text mining has evolved significantly over decades, extracting knowledge from unstructured text.
  • Neural networks and deep learning have driven major advancements in Natural Language Processing (NLP).
  • Recent breakthroughs in the last five years have enabled practical transfer learning applications in NLP.

Purpose of the Study:

  • To provide a targeted literature review of key techniques in NLP.
  • To explain the advancements that led to outperforming human baseline performance.
  • To contextualize neural language models contributing to general language representation.

Main Methods:

  • Targeted literature review.
  • Analysis of neural network and deep learning techniques in NLP.
  • Review of transfer learning methodologies in language models.

Main Results:

  • Neural networks and deep learning have achieved substantial progress in NLP.
  • Transfer learning techniques are now practically applicable.
  • Human baseline performance in general language understanding has been surpassed.

Conclusions:

  • Key techniques reviewed represent vital steps towards general language representation models.
  • The practical application of transfer learning marks a significant milestone in NLP.
  • Continued research in neural language models is crucial for advancing AI language capabilities.