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Language and Cognition01:27

Language and Cognition

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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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A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English
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iSentenizer-μ: multilingual sentence boundary detection model.

Derek F Wong1, Lidia S Chao1, Xiaodong Zeng1

  • 1NLPCT Laboratory, Department of Computer and Information Science, University of Macau, Macau.

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Summary

A new multilingual sentence boundary detection (SBD) system, iSentenizer-μ, accurately processes mixed genres and languages. It uses incremental learning to adapt without retraining, outperforming existing SBD models.

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics

Background:

  • Sentence boundary detection (SBD) systems are typically sensitive to training data genres and languages.
  • Retraining SBD models for new data requires discarding previous work and starting from scratch.

Purpose of the Study:

  • To introduce iSentenizer-μ, a novel multilingual SBD system.
  • To develop an adaptable SBD system capable of handling diverse text genres and languages.

Main Methods:

  • Utilized an incremental tree learning architecture, specifically the i(+)Learning algorithm.
  • Developed a system adaptable to various text topics and Roman-alphabet languages through incremental knowledge merging.
  • Designed iSentenizer-μ to revise existing models rather than requiring complete retraining.

Main Results:

  • iSentenizer-μ demonstrated high accuracy in detecting sentence boundaries across a mixture of text genres and languages.
  • The system was extensively evaluated on Danish, German, English, Spanish, Dutch, French, Italian, Portuguese, Greek, Finnish, and Swedish.
  • Outperformed two state-of-the-art SBD systems, Punkt and MaxEnt, on all tested datasets.

Conclusions:

  • The proposed iSentenizer-μ system offers a robust and adaptable solution for multilingual SBD.
  • Incremental learning enables efficient adaptation to new data, overcoming limitations of traditional retraining approaches.
  • iSentenizer-μ provides superior performance compared to existing SBD systems in diverse linguistic and topical contexts.