Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A reduced ambiguity lexical system.

Paul Frenger1

  • 1A Working Hypothesis, Inc., P.O. Box 820506, Houston, TX 77282, USA. pfrenger@alumni.rice.edu

Biomedical Sciences Instrumentation
|May 12, 2004
PubMed
Summary

This study introduces a novel byte-coded representation for human language, improving AI processing. This method enhances natural language processing (NLP) for applications like speech recognition and machine translation.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Inexpensive Complex Hand Model Twenty Years Later.

Biomedical sciences instrumentation·2015
Same author

Nasa astronauts, prosthetics and the manned space program.

Biomedical sciences instrumentation·2014
Same author

Reviving a medical wearable computer for teaching purposes.

Biomedical sciences instrumentation·2014
Same author

Hacking medical devices a review - biomed 2013.

Biomedical sciences instrumentation·2013
Same author

Human sexual function emulator - biomed 2011.

Biomedical sciences instrumentation·2011
Same author

Emulating I - biomed 2010.

Biomedical sciences instrumentation·2010

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Natural human languages present challenges for AI due to imprecise meaning representation.
  • Existing AI systems struggle with the nuances of diverse human languages, including slang and dialects.

Purpose of the Study:

  • To develop a technique for converting human language into a compact, byte-coded intermediate representation for easier computer processing.
  • To create a specialized lexical engine supporting multiple alphabets, grammars, and pronunciation rules for broad language compatibility.

Main Methods:

  • A novel byte-coding technique was devised for human language.
  • A specialized lexical engine, based on IEEE Standard 1275-1994, was developed.
  • Redundant information was invisibly embedded within the byte-coded text stream.

Main Results:

  • The byte-coded representation facilitates more efficient processing by computer systems.
  • The system supports very large vocabularies across various human languages.
  • The lexical tools enable the use of diverse alphabets, grammars, and pronunciation rules, including regional dialects and slang.

Conclusions:

  • The developed technique offers a more robust and efficient way to represent human language for AI.
  • This approach can significantly enhance the performance of speech recognition, speech synthesis, universal translators, and machine intelligence systems.
  • The system's flexibility supports a wide range of linguistic variations, paving the way for more sophisticated AI language capabilities.

Related Experiment Videos