HiddenPage Making the Training Text

Making the Training Text

The training text should be a plain text file containing text `similar' to what you intend to write. The larger the better. We think that 300K is a good size to aim for. Our preferred file format for all corpuses is UTF-8, but if you prefer to provide another format, that's OK; when supplying the file please include a description of its contents and name the format; we'll use the linux utility iconv to convert, if necessary.

Example training texts that you could use are:

Take all the documents you have written, and glue them all together in one big document.
Use novels - eg, we used Jane Austen's Emma from Project Gutenberg. The problem with using just one or two novels, however, is that particular words (like Emma or Alice) occur very frequently; so novels are not ideal for a general-purpose training text.
Use all the email messages you have written, and glue them all together in one big document..

Existing corpuses

There is a corpora mailing list; this website has lots of useful links.

How to make a general-purpose training text

You can make a pretty good corpus simply by concatenating a load of documents in your chosen language. Such a corpus is pretty good, but not ideal, since, for example, if you include all of Alice in Wonderland, the word Alice and the phrase white rabbit will occur far more often than normal. The aims of the more complicated procedure described below are

to create a corpus that has all common words represented in a variety of contexts, with no one source document dominating the statistics;
to create a corpus that can be sensibly shrunk to make a smaller corpus (for handheld computers with small memory, for example).

Here's how I made the training text for the English version of Dasher.

Get lots of English documents. Get far more material than you think you need, so that we can select a well-balanced set of sentences in a sensible way, as follows.
Pre-process them all so that there is exactly one sentence per line.
I did this using a perl program I wrote, processbook.p with scripts like this
```
foreach f ( alice emma )
  processbook.p  /books0/$f > /books/$f
end
```
Now, obtain a listing of the 2000 most frequent words in the language. The idea is, since these words are common, it is important that we should have them represented several times each in the final corpus, in a variety of contexts. We will use these words to select which sentences are included from our over-large corpus.
I obtained such a list from the internet and put it in a file called dict. I removed from dict any absurdly common words that prevented the remaining steps from working nicely.
Use another program to select from each pre-processed book the sentences that contain the 2000 required words. Go through the required words in order, so that the resulting corpus is also ordered, with the top of the corpus containing examples of use of the most common words; that way, the corpus can be shrunk by cutting its tail off, and should still be an appropriate corpus for its size.
Glue the sentences together into plausible-sized paragraphs, so as to emulate normal writing.
I did this step by using the linux utility glimpse and my perl program corpus.p
```
rm  /data/coll/mackay/books/*~
glimpseindex -b  -B   -H ~/dasher/  /data/coll/mackay/books/
corpus.p k=1 f=4 o=corpus4.txt
       
```
That's how I made this corpus (316K), which is used in Dasher 1.6.8.
If you have any non-ASCII characters, you need to convert the file to UTF-8 (or send it to us so we can do it). iconv is a Unix tool that can do this. If your text is in ISO-8859-1 format (ie, Western Europe), run
```
iconv -f iso-8859-1 -t utf-8 corpus >corpus.utf8
```
which will produce a UTF-8 version of the corpus in corpus.utf8.

If people make good corpuses in other languages and wish to share them, I can put them on this site.