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The Life Foundations Nexus

 

 

HOW A COMPUTER PROGRAM DETERMINES LANGUAGE EQUIVALENTS

 

 

Copyright July 27, 2006 7:34 AM CST

By Dr. Michael J. Bisconti

 

 

 

A computer determines language equivalents the same way a human being determines language equivalents except that it can do it more precisely and much faster.  How does a person determine language equivalents?

 

 

Human Equivalent Language Determination

 

In order for you and I to determine language equivalents we need:

 

·        The expression we want to convert to equivalent language

 

·        Potential equivalent language

 

For example, I want to convert “My dog has fleas” to some equivalent language.  Well, what are my PELUs (which stands for “Potential Equivalent Linguistic Units” and is pronounced “PAL-LOSE” [singular “PAL-LU”])?  Here are some:

 

1.      The canine with which I live is carrying small wingless bloodsucking insects.

 

2.      The canine I own is carrying small wingless bloodsucking insects.

 

3.      The canine with which I live is carrying insects that are small, wingless, and bloodsucking.

 

You will note that any of these three PELUs would serve as a language equivalent (technically referred to as an ELU [Equivalent Linguistic Unit {pronounced “E-LU,” which rhymes with “ME TOO”}]).  Once the PELUs have been identified the average person would easily select any of the PELUs as the ELU for “My dog has fleas.”

 

 

Computer Equivalent Language Determination

 

Now what is extremely simple and straightforward for a human being is NOT so for a computer.  This is because a computer lacks understanding.  In place of understanding the computer must use an MPM (Meaning Probability Matrix) to mimic human understanding.  The computer assigns meaning based on “the most probable use of language.”  This is the same way that human beings assign meaning but we do it unconsciously.

 

Well, in order to create an MPM we need to generate meaning probabilities possessing extremely high reliability.  In order to do this we need massive amounts of data.   We get this data from the “2005 Majority Usage Standard.”  The “2005MU/Std” (2005 Majority Usage Standard) is the result of the surveying of OVER TEN MILLION SOURCES in over twenty English-speaking countries.  Each source provides anywhere from a thousand to ten thousand pieces of information.  The result is a database containing around 70 billion pieces of linguistic data.  This data includes “hooks” for every “common expression” and every SCE (“Specialized, Common Expression” [more on this later]) in the English language.  These hooks indicate the most likely UCs (Usage Contexts) for each expression.

 

The expression for which you are seeking an equivalent expression is referred to as the “infant.”  When a computer program seeks a PELU (see above) for an infant it looks for every expression that “owns” the same context as the infant.  Each of these expressions is assigned a numerical value (“context index”) that tells how likely it is that the expression is a fit.  The higher the context index (numerical value) the more likely the computer program will select that PELU.  Here is an example based on the human example above:

 

INFANT

 

My dog has fleas

 

PELUs

 

PELU HOOK MATRIX

Linguistic Unit

Context Description

Hooks (f©)

The

a neutral word having no hooks

0

Canine

ten standard contexts

21

With

a neutral word having no hooks

0

Which

a neutral word having no hooks

0

I

ten thousand standard contexts

100

Live

twenty thousand standard contexts

200

Is

one billion standard contexts

100,000

Carrying

two thousand standard contexts

50

Small

one hundred thousand standard contexts

125

Wingless

two hundred standard contexts

10

Bloodsucking

twenty standard contexts

5

Insects

one thousand standard contexts

300

 

Using the PELU Hook values the computer program determines a set of probabilities of how likely it is that a given linguistic unit will be a language equivalent (ELU) for the infant “My dog has fleas.”  In order to do this it interacts with an Infant Hook Matrix (not shown on this page but like the one above).  Once an EPM (“Exhaustive Probability Matrix” [all possible probabilities for all linguistic units in the 2005MU/Std Database {see above}]) is created the program sorts the data and selects the entries having the highest probability.  If we were to stop here, the program would only be 95% successful.  However, we have added a RAG (“Ratio Analyzing Program”) that recursively brings the success rate up to 100%.

 

This page just gives you a taste of what is involved in the computer determination of language equivalents.