File: Crawler.txt

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NOTES ON THE CONSTRUCTION OF THE WORD LIST
   A preliminary version of this spell checking dictionary was assembled
with the help of my web crawler "An Crbadn":

  http://borel.slu.edu/crubadan/

BUILDING TEXT CORPORA FOR MINORITY LANGUAGES

   Initially a small collection of "seed" texts are fed to the crawler
(a few hundred words of running text have been sufficient in practice).
Queries combining words from these texts are generated and passed to
the Google API which returns a list of documents potentially written
in the target language.  These are downloaded, processed into plain text,
and formatted.  A combination of statistical techniques bootstrapped from
the initial seed texts (and refined as more texts are added to the database)
is used to determine which documents (or sections thereof) are written in
the target language.   The crawler then recursively follows links contained
within documents that are in the target language.   When these run out,
the entire process is repeated, with a new set of Google queries generated
from the new, larger corpus.

EXTRACTING A CLEAN WORD LIST

   The raw texts downloaded using the scheme just described contain a lot
of pollution and are unsuitable for use without some further processing.   
I have been able to extract reasonably accurate spell checking dictionaries
by applying a series of simple filters.   
   First, statistics measuring co-occurrence with the highest frequency words
in the target language are used to filter out sections written in other
languages or containing mostly noise (e.g. computer code, tabular data, etc.).
The remaining text is tokenized and used to generate a word list sorted by
frequency and the lowest frequency words are filtered out.   Then, depending
on the target language, correctly-spelled words from one or more "polluting"
languages are filtered out to be checked by hand later.  Usually this means
English, but I also filter Dutch from the Frisian corpus, Spanish from
Chamorro, etc.  The remaining words are used to generate 3-gram statistics
for the target language.  These are used to flag as "suspect" any remaining
words containing one or more improbable 3-grams.  Additional filters specific
to certain languages can be applied optionally; for instance, pairs of words
differing only in the presence or absence of diacritical marks can be flagged,
or words with a capital letter appearing after the first letter, words
with no vowels, etc.

Please contact me at the address below if you are interested in applying
these techniques to a new language.

Kevin Scannell 
<scannell@slu.edu>
March 2004