Scientific Biblical Studies - Advanced Studies
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The Life Foundations
Nexus
HOW A COMPUTER PROGRAM DETERMINES LANGUAGE
EQUIVALENTS
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.