Provided by: libtext-ngrams-perl_2.006-1_all bug


       Text::Ngrams - Flexible Ngram analysis (for characters, words, and more)


       For default character n-gram analysis of string:

         use Text::Ngrams;
         my $ng = Text::Ngrams->new;
         print $ng3->to_string;
         my @ngramsarray = $ng->get_ngrams;
         # or put ngrams and frequencies into a hash
         my %ngrams = $ng3->get_ngrams( n => 3, normalize => 1 );

       One can also feed tokens manually:

         use Text::Ngrams;
         my $ng3 = Text::Ngrams->new;
         $ng3->feed_tokens('c', 'd');
         $ng3->feed_tokens(qw(e f g h));

       We can choose n-grams of various sizes, e.g.:

         my $ng = Text::Ngrams->new( windowsize => 6 );

       or different types of n-grams, e.g.:

         my $ng = Text::Ngrams->new( type => byte );
         my $ng = Text::Ngrams->new( type => word );
         my $ng = Text::Ngrams->new( type => utf8 );

       To process a list of files:

         $ng->process_files('somefile.txt', 'otherfile.txt');

       To read the standard input or another file handle:


       To read a file named file.txt and create a profile file file.profile of 100 most frequent,
       normalized byte tri-grams:

         use Text::Ngrams;
         my $ng = Text::Ngrams->new( windowsize => 3, type => byte );
         $ng->to_string( orderby=>'frequency', onlyfirst=>100,
                       out => "file.profile", normalize=>1,


       This module implement text n-gram analysis, supporting several types of analysis,
       including character and word n-grams.

       The module Text::Ngrams is very flexible.  For example, it allows a user to manually feed
       a sequence of any tokens.  It handles several types of tokens (character, word), and also
       allows a lot of flexibility in automatic recognition and feed of tokens and the way they
       are combined in an n-gram.  It counts all n-gram frequencies up to the maximal specified
       length.  The output format is meant to be pretty much human-readable, while also loadable
       by the module.

       The module can be used from the command line through the script "" provided with
       the package.


       The output looks like this (version number may be different):

         BEGIN OUTPUT BY Text::Ngrams version 2.004

         1-GRAMS (total count: 8)
         a     1
         b     1
         c     1
         d     1
         e     1
         f     1
         g     1
         h     1

         2-GRAMS (total count: 7)
         ab    1
         bc    1
         cd    1
         de    1
         ef    1
         fg    1
         gh    1

         3-GRAMS (total count: 6)
         abc   1
         bcd   1
         cde   1
         def   1
         efg   1
         fgh   1

         END OUTPUT BY Text::Ngrams

       N-grams are encoded using encode_S (, so that
       they can always be recognized as \S+.  This encoding does not change strings "too much",
       e.g., letters, digits, and most punctuation characters will remail unchanged, and space is
       replaced by underscore (_).  However, all bytes (even with code greater than 127) are
       encoded in unambiguous and relatively compact way.  Two functions, encode_S and decode_S,
       are provided for translating arbitrary string into this form and vice versa.

       An example of word n-grams containing space:

         BEGIN OUTPUT BY Text::Ngrams version 2.004

         1-GRAMS (total count: 8)
         The   1
         brown 3
         fox   3
         quick 1

         2-GRAMS (total count: 7)
         The_brown     1
         brown_fox     2
         brown_quick   1
         fox_brown     2
         quick_fox     1

         END OUTPUT BY Text::Ngrams

       Or, in case of byte type of processing:

         BEGIN OUTPUT BY Text::Ngrams version 2.004

         1-GRAMS (total count: 55)
         \t    3
         \n    3
         _     12
         ,     2
         .     3
         T     1
         b     3
         c     1
         ... etc

         2-GRAMS (total count: 54)
         \t_   1
         \tT   1
         \tb   1
         \n\t  2
         __    5
         _.    1
         _b    2
         _f    3
         _q    1
         ,\n   2
         .\n   1
         ..    2
         Th    1
         br    3
         ck    1
         e_    1
         ... etc

         END OUTPUT BY Text::Ngrams


   new ( windowsize => POS_INTEGER, type => 'character' | 'byte' | 'word' | 'utf8' |
       'utf8_character', limit => POS_INTEGER )
         my $ng = Text::Ngrams->new;
         my $ng = Text::Ngrams->new( windowsize=>10 );
         my $ng = Text::Ngrams->new( type=>'word' );
         my $ng = Text::Ngrams->new( limit=>10000 );
         and similar.

       Creates a new "Text::Ngrams" object and returns it.  Parameters:

           Limit the number of distinct n-grams collected during processing.  Processing large
           files may be slow, so you can limit the total number of distinct n-grams which are
           counted to speed up processing.  The speed-up is implemented by periodically prunning
           the collected n-gram.  Due to this process, the final n-gram counts may not be
           correct, and the list of final most frequen n-grams may not be correct either.

           BEWARE: If a limit is set, the n-gram counts at the end may not be correct due to
           periodical pruning of n-grams.

           n-gram size (i.e., `n' itself).  Default is 3 if not given.  It is stored in

           Specifies a predefined type of n-grams:

           character (default)
               Default character n-grams: Read letters, sequences of all other characters are
               replaced by a space, letters are turned uppercase.

               Raw character n-grams: Don't ignore any bytes and don't pre-process them.

               UTF8 characters: Variable length encoding.

               Default word n-grams: One token is a word consisting of letters, digits and
               decimal digit are replaced by <NUMBER>, and everything else is ignored.  A space
               is inserted when n-grams are formed.

               UTF8 analogue of the "character" type: from a UTF8 encoded text reads letters,
               sequences of all other characters are replaced by a space, letters are turned

           One can also modify type, creating its own type, by fine-tuning several parameters
           (they can be undefined):

           $o->{skiprex} - regular expression for ignoring stuff between tokens.

           $o->{skipinsert} - string to replace a skiprex match that makes
               string too short (efficiency issue)

           $o->{tokenrex} - regular expression for recognizing a token.  If it is empty, it means
           chopping off one character.

           $o->{processtoken} - routine for token preprocessing.  Token is given and returned in

           $o->{allow_iproc} - boolean, if set to true (1) allows for incomplete
               tokens to be preprocessed and put back (efficiency motivation)

           $o->{inputlayer} - input layer to be put on the input stream by the function binmode
               before reading from a given stream and to be removed by ***binmode
               after the reading from the particular stream is done.
               Has to be a real layer (like ":encoding(utf8)"), not a pseudo layer (like ":utf8")
               so that the pseudo layer ":pop" is able to remove this input layer

           For example, the types character, byte, and word are defined in the foolowing way:

             if ($params{type} eq 'character') {
                 $self->{skiprex} = '';
                 $self->{tokenrex} = qr/([a-zA-Z]|[^a-zA-Z]+)/;
                 $self->{processtoken} =  sub { s/[^a-zA-Z]+/ /; $_ = uc $_ }
                 $self->{allow_iproc} = 1;
             elsif ($params{type} eq 'byte') {
                 $self->{skiprex} = '';
                 $self->{tokenrex} = '';
                 $self->{processtoken} = '';
             elsif ($params{type} eq 'utf8') {
                 $self->{skiprex} = '';
                 $self->{tokenrex} =
                 $self->{processtoken} = '';
             elsif ($params{type} eq 'word') {
                 $self->{skiprex} = qr/[^a-zA-Z0-9]+/;
                 $self->{skipinsert} = ' ';
                 $self->{tokenrex} =
                 $self->{processtoken} = sub
                   { s/(\d+(\.\d+)?|\d*\.\d+)([eE][-+]?\d+)?/<NUMBER>/ }

   feed_tokens ( list of tokens )
         $ng3->feed_tokens('b', 'c');

       This function supplies tokens directly.

   process_text ( list of strings )
         $ng->process_text('The brown quick fox, brown fox, brown fox ...');

       Process text, i.e., break each string into tokens and feed them.

   process_files ( file_names or file_handle_references)
       A usage example:


       This method is used to process one or more files, similarly to processing text.  The files
       are processed line by line, so there should be no multi-line tokens.  Instead of filenames
       we can also give as arguments file handle references when a file is already open.  In this
       way, we can use the standard input handle as in:


   get_ngrams ( n => NUMBER, orderby => 'ngram|frequency|none', onlyfirst => NUMBER, out =>
       Returns an array of requested n-grams and their friequencies in order (ngram1, f1, ngram2,
       f2, ...).  The use of parameters is identical to the function "to_string", except that the
       option 'spartan' is not applicable to "get_ngrams" function.


       "n" The parameter "n" specifies the size of n-grams being retrieved.  The default value is
           the "windowsize" field.  It should be less or equal than "windowsize".

   to_string ( orderby => 'ngram|frequency|none', onlyfirst => NUMBER, out => filename|handle,
       normalize => 1, spartan => 1 )
       Some examples:

         print $ng3->to_string;
         print $ng->to_string( orderby=>'frequency' );
         print $ng->to_string( orderby=>'frequency', onlyfirst=>10000 );
         print $ng->to_string( orderby=>'frequency', onlyfirst=>10000,
                               normalize=>1 );

       Produce string representation of the n-gram tables.


           The parameter "orderby" specifies the order of n-grams.  The default value is 'ngram'.

           The parameter "onlyfirst" causes printing only this many first n-grams for each n.  It
           is incompatible with "orderby="'none'>.

           The method "to_string" produces n-gram tables.  However, if those tables are large and
           we know that we will write them to a file right after processing, it may save memory
           and time to provide the parameter "out", which is a filename or reference to a file
           handle.  (Experiments on my machine do not show significant improvement nor
           degradation.)  Filename will be opened and closed, while the file handle will not.

           This is a boolean parameter.  By default, it is false (''), in which case n-gram
           counts are produced.  If it is true (e.g., 1), the output will contain normalized
           frequencies; i.e., n-gram counts divided by the total number of n-grams of the same

           This is a boolean parameter.  By default, it is false (''), in which case n-grams for
           n=1 up to the maximal value are printed.  If it is true, only a list of the most
           frequent n-grams with the maximal length is printed.

   encode_S ( string )
       This function translates any string in a /^\S*$/ compliant representation.  It is
       primarely used in n-grams string representation to prevent white-space characters to
       invalidate the output format.  A usage example is:

         $e = Text::Ngrams::encode_S( $s );

       or simply

         $e = encode_S($s);

       if encode_S is imported.  Encodes arbitrary string into an \S* form.

       See for detailed explanation.

   decode_S ( string )
       This is the inverse funcation of "encode_S".  A usage example is:

         $e = Text::Ngrams::decode_S( $s );

       or simply

         $e = decode_S($s);

       if decode_S is imported.  Decodes a string encoded in the \S* form.

       See for detailed explanation.


       The performance can vary a lot depending on the type of file, in particular on the content
       entropy.  For example a file in English is processed faster than a file in Chinese, due to
       a larger number of distinct n-grams.

       The following tests are preformed on a Pentium-III 550MHz, 512MB memory, Linux Red Hat 6
       platform.  (See "" - the script is included in this package.)
 --n=10 --type=byte 1Mfile

       The 1Mfile is a 1MB file of Chinese text.  The program spent consistently 20 sec per
       100KB, giving 200 seconds (3min and 20sec) for the whole file.  However, after 4 minutes I
       gave up on waiting for n-grams to be printed.  The bottleneck seems to be encode_S
       function, so after:
 -n=10 --type=byte --orderby=frequency --onlyfirst=5000

       it took about 3:24 + 5 =~ 9 minutes to print.  After changing "" so that it
       provides parameter "out" to "to_string" in module "" (see Text::Ngrams), it still
       took: 3:09+1:28+4:40=9.17.


       The method "process_file" does not handle multi-line tokens by default.  This can be
       fixed, but it does not seem to be worth the code complication.  There are various ways
       around this if one really needs such tokens:  One way is to preprocess them.  Another way
       is to read as much text as necessary at a time then to use "process_text", which does
       handle multi-line tokens.


       I would like to thank cpan-testers, Jost Kriege, Shlomo Yona, David Allen (for localizing
       and reporting and efficiency issue with ngram prunning), Andrija, Roger Zhang, Jeremy
       Moses, Kevin J. Ziese, Hassen Bouzgou, Michael Ricie, and Jingyi Yang for bug reports and

       Thanks to Chris Jordan for providing initial implementation of the function get_strings

       Thanks to Magdalenda Jankowska for implementing a new ngrams type utf8_character, which is
       very useful in processing non-English text; and for a bug fix.

       I will be grateful for comments, bug reports, or just letting me know that you used the



        2003-2017 Vlado Keselj


             2005 Chris Jordan (contributed initial get_ngrams method)
             2012 Magdalena Jankowska (utf8_character ngrams type)

       This module is provided "as is" without expressed or implied warranty.  This is free
       software; you can redistribute it and/or modify it under the same terms as Perl itself.

       To acknowledge the use of this module in academic publications, please use a reference to
       the following paper:

       N-gram-based Author Profiles for Authorship Attribution.  Vlado Keselj, Fuchun Peng, Nick
       Cercone, and Calvin Thomas. In Proceedings of the Conference Pacific Association for
       Computational Linguistics, PACLING'03, Dalhousie University, Halifax, Nova Scotia, Canada,
       pp. 255-264, August 2003.

       The latest version can be found at


       This code originated in my "monkeys and rhinos" project in 2000, and is related to
       authorship attribution project.  After our papers on authorship attribution it was
       reformatted as a Perl module in 2003.


       Some of the similar projects and related resources are the following:

       Ngram Statistics Package in Perl, by T. Pedersen at al.
           This is a package that includes a script for word n-grams.

       Text::Ngram Perl Package by Simon Cozens
           This is another CPAN package similar to Text::Ngrams for character n-grams.  As an XS
           implementation it is supposed to be very efficient.

       Perl script by Jarkko Hietaniemi
           This is a script for analyzing character n-grams.

       Waterloo Statistical N-Gram Language Modeling Toolkit, in C++ by Fuchun Peng
           A n-gram language modeling package written in C++.

       CPAN N-gram module comparison article by Ben Bullock.
           The page is available
             at gives an interesting list of
             modules, although the review seem to be superficial and only partially correct.
             The following modules are listed in this review:
             Algorithm::NGram, IDS::Algorithm::Ngram, Lingua::EN::Bigram, Linuga::EN::Ngram,
             Lingua::Gram, Lingua::Identify, Text::Mining::Algorithm::Ngram,
             Text::Ngram, Text::Ngram::LanguageDetermine, Text::Ngramize, Ntext::Ngrams, and

       Some links to these resources should be available at