Markov Chain Algorithms
not very technical explanation)
How the algorithms work.
Markov algorithms do not require either mathematics or computers. It is
possible to perform a Markov text generation algorithm with a pencil
and paper (I will
discuss how to do this below). The only other thing required is an
input text. There are mathematically oriented accounts available that
also discuss such matters. This one will avoid equations entirely. To
illustrate my discussion, I will use this
Markov algorithms work with patterns.
Markov algorithms determine how likely it is that a word will follow
another. What happens when it is equally likely that one word
will follow as another one? The answer is it tosses a coin, or it makes
a random choice, to put it another way.
Example: in the first paragraph the word “will” appears three
times. It is followed by
If a Markov algorithm were to encounter the word “will” in the above,
it could randomly choose one of these three words available to it as
being equally probable. It might then take for instance the word
“discuss” and follow it up with either “how” or “such”. It continues to
do this each time, taking a word, finding one to follow it, taking the
last word found and then adding another. As you see, it deals with one
pair of words at a time. It adds a word and this makes a new pair. (It
is possible for the algorithm to use threes and fours or more, but
pairs seem to produce more interesting results.)
With short texts, like the first paragraph, it doesn’t have many
options available to it as most of the words appear only once. But with
longer texts it can produce many variations.
The texts it produces seem often quite like the source text. Frequently
they are also rather strange sounding.
One of the things about a Markov chain algorithm is that it treats
punctuation as part of a word. So, it would treat “word.” as a word, if
you see what I mean. This is quite useful as frequently it manages to
punctuate plausibly by shifting letters and punctuation marks together.
Sometimes it does not though and can put punctuation marks in funny
Markov algorithms have often been used to produce texts that are both
nonsensical and rather plausible.
A quite well known example is “Mark V. Shaney”. He is discussed in:
Kernighan, Brian W and Pike, R. (1999) The Practice of
Reading, MA, Addison-Wesley.
Apparently he confused people who thought he was ‘real’. He’s still out
there living on the web somewhere.
A Few Observations.
My own use of Kernighan and Pike’s algorithm differs quite a bit in
that I am not really interested in hoodwinking. The text that my Markov Generator
uses is my own research into text generation. I was trying to generate
a text that was a text generation text…
A slightly more technical point is that Markov algorithms tend to
always start with the same word. This is repetitious of them after a
while. So I run my text through a ‘shuffle’ first. This stops it doing
Markov algorithms are only as syntactical – at best – as the texts they
use. If the text that is fed in is only one word repeated a lot, the
algorithm will only produce the same text. If the text consists of a
jumble of words appearing with equal frequency, then the text made is
not likely to be any more like English than the input. (Obviously in
English for example we choose words with slightly more care.)
In other words Markov algorithms just do a calculation. They cannot
produce sentences for themselves. For that you need something like a
recursive grammar (my Virtual
Dictionary uses this to make its texts). These should always
grammatical sentences as long as they are written that way. They
probably produce nonsense too, however. See, Bulhak, A.C. (1996) On
the Simulation of Postmodernism and Mental Debility using Recursive
Transition Networks. Natural Language Programming, tries to
overcome this. But that is the subject of another discussion.
Lastly, Markov algorithms have a very short memory. That is to say,
they produce a text based on their count of word frequencies. Each time
the algorithm is run, it starts anew. For this reason Markov processes
are called 'finite state machines': each state is determined by the one
previous. A Markov algorithm only looks through a text on a pair by
word pair basis (although it can handle longer sequences). If you want
something that seems to live and grow, you might be interested in
artificial life programming.
Shannon’s Pen and Paper Markov Method.
Shannon is the founder of Information Theory. He wrote a (1948)
paper, ‘A Mathematical Theory of Communication’ where he explains
the basic technique (although this version is a little different).
Choose a pair of words at random from a novel. Read through the novel
until the second of the words is encountered again. Write down the word
that follows it. Carry on until you hit the word you just wrote down.
When you find it write down the word that follows that word. Continue
until you make something interesting or exhaustion sets in.
This is based on a now obscure text by J. R. Pierce describing
Shannon’s techniques. See ‘A chance for art’, in Cybernetics, art
and ideas, (1971) Jasia Reichardt (Ed.), London, Studio Vista.
The method may produce mostly garbage with occasional more interesting
passages. It’s also very slow. Easier to get a computer to do it?