SWI-Prolog Natural Language Processing Primitives
Jan Wielemaker
VU University Amsterdam
The Netherlands
E-mail: J.Wielemaker@cs.vu.nl
Abstract
This package contains some well known basic routines for natural language processing and information retrieval. The current version of this package is very limited, which makes the name somewhat misleading. Suggestions and contributions are welcome.

# Table of Contents

## 1 Double Metaphone -- Phonetic string matching

The library `library(double_metaphone)` implements the Double Metaphone algorithm developed by Lawrence Philips and described in “The Double-Metaphone Search Algorithm” by L Philips, C/C++ User's Journal, 2000. Double Metaphone creates a key from a word that represents its phonetic properties. Two words with the same Double Metaphone are supposed to sound similar. The Double Metaphone algorithm is an improved version of the Soundex algorithm.

double_metaphone(+In, -MetaPhone)
  Same as double_metaphone/3, but only returning the primary metaphone.
double_metaphone(+In, -MetaPhone, -AltMetaphone)
  Create metaphone and alternative metaphone from In. The primary metaphone is based on english, while the secondary deals with common alternative pronounciation in other languages. In is either and atom, string object, code- or character list. The metaphones are always returned as atoms.

### 1.1 Origin and Copyright

The Double Metaphone algorithm is copied from the Perl library that holds the following copyright notice. To the best of our knowledge the Perl license is compatible to the SWI-Prolog license schema and therefore including this module poses no additional license conditions.

> Copyright 2000, Maurice Aubrey <maurice@hevanet.com>. All rights reserved.

> This code is based heavily on the C++ implementation by Lawrence Philips and incorporates several bug fixes courtesy of Kevin Atkinson <kevina@users.sourceforge.net>.

> This module is free software; you may redistribute it and/or modify it under the same terms as Perl itself.

## 2 Porter Stem -- Determine stem and related routines

The `library(porter_stem)` library implements the stemming algorithm described by Porter in Porter, 1980, “An algorithm for suffix stripping” , Program, Vol. 14, no. 3, pp 130-137. The library comes with some additional predicates that are commonly used in the context of stemming.

porter_stem(+In, -Stem)
  Determine the stem of In. In must represent ISO Latin-1 text. The porter_stem/2 predicate first maps In to lower case, then removes all accents as in unaccent_atom/2 and finally applies the Porter stem algorithm.
unaccent_atom(+In, -ASCII)
  If In is general ISO Latin-1 text with accents, ASCII is unified with a plain ASCII version of the string. Note that the current version only deals with ISO Latin-1 atoms.
tokenize_atom(+In, -TokenList)
  Break the text In into words, numbers and punctuation characters. Tokens are created to the following rules:

  `[-+][0-9]+(\.[0-9]+)?([eE][-+][0-9]+)?` | number
  `[:alpha:][:alnum:]+` | word
  `[:space:]+` | skipped
  anything else | single-character

  Character classification is based on the C-library iswalnum() etc. functions. Recognised numbers are passed to Prolog read/1, supporting unbounded integers.

  It is likely that future versions of this library will provide tokenize_atom/3 with additional options to modify space handling as well as the definition of words.

atom_to_stem_list(+In, -ListOfStems)
  Combines the three above routines, returning a list holding an atom with the stem of each word encountered and numbers for encountered numbers.

### 2.1 Origin and Copyright

The code is based on the original Public Domain implementation by Martin Porter as can be found at http://www.tartarus.org/martin/PorterStemmer/. The code has been modified by Jan Wielemaker. He removed all global variables to make the code thread-safe, added the unaccent and tokenize code and created the SWI-Prolog binding.

## 3 library(snowball): The Snowball multi-lingual stemmer library

See also
  http://snowball.tartarus.org/

This module encapsulates "The C version of the libstemmer library" from the Snowball project. This library provides stemmers in a variety of languages. The interface to this library is very simple:

- snowball/3 stems a word with a given algorithm
- snowball_current_algorithm/1 enumerates the provided algorithms.

Here is an example:

    ?- snowball(english, walking, S).
    S = walk.

[det]snowball(+Algorithm, +Input, -Stem)
  Apply the Snowball Algorithm on Input and unify the result (an atom) with Stem.

  The implementation maintains a cache of stemmers for each thread that accesses snowball/3, providing high-perfomance and thread-safety without locking.

  Algorithm | is the (english) name for desired algorithm or an 2 or 3 letter ISO 639 language code.
  Input | is the word to be stemmed. It is either an atom, string or list of chars/codes. The library accepts Unicode characters. Input must be lowercase. See downcase_atom/2.

  Errors
    - `domain_error(snowball_algorithm, Algorithm)`
    - `type_error(atom, Algorithm)`
    - `type_error(text, Input)`
[nondet]snowball_current_algorithm(?Algorithm)
  True if Algorithm is the official name of an algorithm suported by snowball/3. The predicate is `semidet` if Algorithm is given.

## 4 library(isub): isub: a string similarity measure

author
  Giorgos Stoilos
See also
  A string metric for ontology alignment by Giorgos Stoilos, 2005 - http://www.image.ece.ntua.gr/papers/378.pdf .

The `library(isub)` implements a similarity measure between strings, i.e., something similar to the Levenshtein distance. This method is based on the length of common substrings.

[det]isub(+Text1:text, +Text2:text, -Similarity:float, +Options:list)
  Similarity is a measure of the similarity/dissimilarity between Text1 and Text2. E.g.

      ?- isub('E56.Language', 'languange', D, [normalize(true)]).
      D = 0.4226950354609929.                       % [-1,1] range

      ?- isub('E56.Language', 'languange', D, [normalize(true),zero_to_one(true)]).
      D = 0.7113475177304964.                       % [0,1] range

      ?- isub('E56.Language', 'languange', D, []).  % without normalization
      D = 0.19047619047619047.                      % [-1,1] range

      ?- isub(aa, aa, D, []).  % does not work for short substrings
      D = -0.8.

      ?- isub(aa, aa, D, [substring_threshold(0)]). % works with short substrings
      D = 1.0.                                      % but may give unwanted values
                                                    % between e.g. 'store' and 'spore'.

      ?- isub(joe, hoe, D, [substring_threshold(0)]).
      D = 0.5315315315315314.

      ?- isub(joe, hoe, D, []).
      D = -1.0.

  This is a new version of isub/4 which replaces the old version while providing backwards compatibility. This new version allows several options to tweak the algorithm.

  Text1 | and Text2 are either an atom, string or a list of characters or character codes.
  Similarity | is a float in the range [-1,1.0], where 1.0 means most similar. The range can be set to [0,1] with the zero_to_one option described below.
  Options | is a list with elements described below. Please note that the options are processed at compile time using goal_expansion to provide much better speed. Supported options are: normalize(+Boolean) Applies string normalization as implemented by the original authors: Text1 and Text2 are mapped to lowercase and the characters "._ " are removed. Lowercase mapping is done with the C-library function `towlower()`. In general, the required normalization is domain dependent and is better left to the caller. See e.g., unaccent_atom/2. The default is to skip normalization (`false`). zero_to_one(+Boolean) The old isub implementation deviated from the original algorithm by returning a value in the [0,1] range. This new isub/4 implementation defaults to the original range of [-1,1], but this option can be set to `true` to set the output range to [0,1]. substring_threshold(+Nonneg) The original algorithm was meant to compare terms in semantic web ontologies, and it had a hard coded parameter that only considered substring similarities greater than 2 characters. This caused the similarity between, for example’aa’and’aa’to return -0.8 which is not expected. This option allows the user to set any threshold, such as 0, so that the similatiry between short substrings can be properly recognized. The default value is 2 which is what the original algorithm used.

# Index

?
atom_to_stem_list/2
double_metaphone/2
double_metaphone/3
  1
isub/4
porter_stem/2
  2
read/1
  2
snowball/3
snowball_current_algorithm/1
tokenize_atom/2
tokenize_atom/3
  2
unaccent_atom/2
  2