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Related Concept Videos

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 and Cognition01:27

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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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Language Development01:22

Language Development

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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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Language01:16

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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.
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Nominal Level of Measurement00:56

Nominal Level of Measurement

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The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Lexical simplification benchmarks for English, Portuguese, and Spanish.

Sanja Štajner1, Daniel Ferrés2, Matthew Shardlow3

  • 1Other, Karlsruhe, Germany.

Frontiers in Artificial Intelligence
|October 10, 2022
PubMed
Summary

A new multilingual dataset for lexical simplification was created. State-of-the-art neural systems outperform non-neural ones, but perform best in English, raising questions for low-resource languages.

Keywords:
artificial intelligence for social goodbenchmark datasetsevaluation methodologieslexical simplificationlow-resource tasksnatural language processing

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Sociolinguistics

Background:

  • Significant portions of the population struggle with complex texts, limiting societal participation and informed decision-making.
  • Lexical simplification (LS) aims to improve text accessibility by replacing difficult words with simpler synonyms.
  • Progress in LS is hindered by a lack of high-quality, comparable datasets across multiple languages.

Purpose of the Study:

  • To introduce a novel benchmark dataset for lexical simplification in English, Spanish, and Brazilian Portuguese.
  • To facilitate direct comparison of LS systems across these three languages using comparable annotation procedures.
  • To enable the creation of similar datasets for other languages and domains.

Main Methods:

  • Development of a multilingual LS dataset with comparable data selection and annotation across English, Spanish, and Brazilian Portuguese.
  • Adaptation of two distinct state-of-the-art LS systems (neural and non-neural) to all three languages.
  • Evaluation of system performance using multiple metrics to assess efficacy across languages.

Main Results:

  • A state-of-the-art neural LS system demonstrated superior performance over a non-neural system across all three languages and evaluation measures.
  • Neural LS systems achieved significantly better results in English compared to Spanish and Brazilian Portuguese.
  • The dataset enables direct comparison of LS system performance across languages.

Conclusions:

  • The created multilingual dataset serves as a valuable resource for advancing lexical simplification research.
  • Neural network architectures show promise for LS but may require language-specific adaptations, particularly for lower-resource languages.
  • Further research is needed to optimize neural LS for languages other than English, especially those with fewer linguistic resources.