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Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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The large ribosomal subunit has several important structures essential to translation. These include the peptidyl transferase center (PTC) - which is the site where the peptide bond is formed - and a large, internal, water-filled tube through which the nascent polypeptide moves. This latter structure is called the Peptide Exit Tunnel, and it begins at the PTC and spans the body of the large ribosomal subunit. During translation, as the nascent polypeptide chain is synthesized, it passes through...
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Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
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Related Experiment Video

Updated: Apr 28, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

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A relationship: word alignment, phrase table, and translation quality.

Liang Tian1, Derek F Wong1, Lidia S Chao1

  • 1Natural Language Processing & Portuguese-Chinese Machine Translation Laboratory, Department of Computer and Information Science, University of Macau, Macau.

Thescientificworldjournal
|June 3, 2014
PubMed
Summary

Researchers developed a formula to predict machine translation phrase table size based on word alignments. A new pruning method significantly reduces phrase tables by 98% with minimal impact on translation quality.

Related Experiment Videos

Last Updated: Apr 28, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Machine Translation

Background:

  • Existing machine translation (MT) quality evaluation methods often lack theoretical grounding.
  • The relationship between word alignments, phrase tables, and MT performance is not well-defined by a formal model.

Purpose of the Study:

  • To formulate a mathematical relationship between word alignments and phrase table size.
  • To propose a corpus-motivated technique for pruning large phrase tables.
  • To provide a theoretical basis for understanding MT performance in relation to phrase table characteristics.

Main Methods:

  • Developing a formula to estimate the number of phrase pairs based on word alignment points.
  • Implementing a corpus-motivated pruning strategy to reduce phrase table redundancy.
  • Conducting experiments to validate the formula and evaluate the pruning technique's effectiveness.

Main Results:

  • The proposed formula accurately estimates phrase table size, offering insights into alignment-performance links.
  • The corpus-motivated pruning technique successfully reduces phrase tables by approximately 98%.
  • This reduction is achieved without significant degradation of machine translation quality.

Conclusions:

  • The established formula provides a theoretical framework for analyzing phrase table properties in MT.
  • The effective pruning method significantly enhances efficiency without compromising translation accuracy.
  • This research offers a valuable reference for future studies on MT performance and phrase table optimization.