Extract words from the NVdB PDF file


Getting the list of words out of the Nuovo vocabolario di base.

The Nuovo vocabolario di base is in PDF form and is arranged as a list that is more or less like this:

1a s.f. e m.inv., 2a prep., abbagliante p.pres., agg., s.m., abbaiare
v.intr. e tr., abbandonare v.tr., abbandonato p.pass., agg., s.m.,
abbandono s.m., abbassare v.tr., abbasso avv., inter., abbastanza avv.,

The first step is extracting the list of words. It’s easy to copy the whole list into a text-only file, but we can use some Perl to remove all the cruft. Here’s my take on this:

#!/usr/bin/env perl
use v5.24;
use warnings;
use English qw< -no_match_vars >;

my $filename = shift // 'vocabolario-di-base-input.txt';

my $text = do {
   open my $fh, '<:encoding(Latin1)', $filename
      or die "open('$filename'): $OS_ERROR\n";
   local $/;

$text =~ s{^\s*[A-Z]\s*[\r\n]*}{, }gmxs;
$text =~ s{ -[\r\n] }{}gmxs;
$text =~ s{ [\r\n]+ }{ }gmxs;

binmode STDOUT, ':encoding(UTF-8)';
my $last = '';
for (split m{ , \s* }mxs, $text) {
   my ($word) = m{\A \d* (\w+) \s+\S}mxs or next;
   say $word if $word ne $last;
   $last = $word;

There’s some heuristic involved, I hope it’s all correct!

  • Different letters are marked in a single line by itself. One substitution gets rid of them, putting a comma character , just to make a single, long list of items.
$text =~ s{^\s*[A-Z]\s*[\r\n]*}{, }gmxs;
  • Some words are hyphenated and split into two parts, to put a newline. We want to reconstruct the original word, so we get rid of the hypens and newlines:
$text =~ s{ -[\r\n] }{}gmxs;
  • Then, we put the whole list on a single line, removing newlines and putting a space instead:
$text =~ s{ [\r\n]+ }{ }gmxs;
  • We’re now ready to go through the list, using the comma and all following spaces as separator. Each item can start with an optional number, then our target word, then something else. Hence, we can get the word with a regular expression:
my ($word) = m{\A \d* (\w+) \s+\S}mxs or next;
  • Some words might appear multiple times because they have different meanings or types (this is where the numbers come into play). We don’t want these duplicates, so we just filter them out:
say $word if $word ne $last;
$last = $word;

Let’s put it at work:


This gives us 7176 items, which I think is a good starting point.

Stay safe!

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