This page was completely revised Jan 2006. The earlier edition is here.
This is the ‘official’ home page for distribution of the Porter Stemming Algorithm, written and maintained by its author, Martin Porter.
The Porter stemming algorithm (or ‘Porter stemmer’) is a process for removing the commoner morphological and inflexional endings from words in English. Its main use is as part of a term normalisation process that is usually done when setting up Information Retrieval systems.
The original stemming algorithm paper was written in 1979 in the Computer Laboratory, Cambridge (England), as part of a larger IR project, and appeared as Chapter 6 of the final project report,
With van Rijsbergen’s encouragement, it was also published in,
- C.J. van Rijsbergen, S.E. Robertson and M.F. Porter, 1980. New models in probabilistic information retrieval. London: British Library. (British Library Research and Development Report, no. 5587).
And since then it has been reprinted in
- M.F. Porter, 1980, An algorithm for suffix stripping, Program, 14(3) pp 130−137.
The original stemmer was written in BCPL, a language once popular, but now defunct. For the first few years after 1980 it was distributed in its BCPL form, via the medium of punched paper tape. Versions in other languages soon began to appear, and by 1999 it was being widely used, quoted and adapted. Unfortunately there were numerous variations in functionality among these versions, and this web page was set up primarily to ‘put the record straight’ and establish a definitive version for distribution.
- Karen Sparck Jones and Peter Willet, 1997, Readings in Information Retrieval, San Francisco: Morgan Kaufmann, ISBN 1-55860-454-4.
The ANSI C version that heads the table below is exactly equivalent to the original BCPL version. The BCPL version did, however, differ in three minor points from the published algorithm and these are clearly marked in the downloadable ANSI C version. They are discussed further below.
This ANSI C version may be regarded as definitive, in that it now acts as a better definition of the algorithm than the original published paper.
Over the years, I have received many encoding from other workers, and they are also presented below. I have a reasonable confidence that all these versions are correctly encoded.
|ANSI C thread safe||me|
|Perl||Daniel van Balen||Oct 1999||slightly faster?|
|python||Vivake Gupta||Jan 2001|
|Csharp||André Hazelwood||The Official Web Guide||Sep 2001|
|Csharp .NET compliant||Leif Azzopardi||Univerity of Paisley, Scotland||Nov 2002|
|Csharp again!||Brad Patton||ratborg.blogspot.com||Dec 2015|| "more like standard|
C# code" (Brad)
|Common Lisp||Steven M. Haflich||Franz Inc||Mar 2002|
|Ruby||Ray Pereda||www.raypereda.com||Jan 2003||github link|
|Visual Basic VB6||Navonil Mustafee||Brunel University||Apr 2003|
|Delphi||Jo Rabin||Apr 2004|
| Visual Basic|
VB7; .NET compliant
| Christos Attikos
|| University of Piraeus, Greece
|| Jan 2005
|| Richard Heyes
|| Feb 2005
|| Philip Brooks
|| University of Georgia
|| Oct 2005
|| Dmitry Antonyuk
|| Nov 2005
|| Keith Lubell
|| May 2006
|| Juan Carlos Lopez
|| California Pacific Medical Center|
| Sep 2006
|| Aris Theodorakos
|| NCSR Demokritos
|| Nov 2006
|| Daniel Truemper
|| Humboldt-Universitaet zu Berlin
|| May 2007
|| erlang (1) erlang (2)
|| Alden Dima
|| National Institute of Standards and|
Technology, Gaithersburg, MD USA
| Sep 2007
|| Dale K Brearcliffe
|| Apr 2009
|| Ken Faulkner
|| May 2009
|| Antoine St-Pierre
|| Business Researchers, Inc
|| Apr 2010
|| plugin vim script
|| Mitchell Bowden
|| May 2010
|| github link
|| Jed Parsons
|| May 2011
|| github link
|| Google Go
|| Alex Gonopolskiy
|| Oct 2011
|| github link
|| Gregory Grefenstette
|| Jul 2012
|| Yushi Wang
|| Mar 2013
|| bitbucket link
|| Do Nhat Minh
|| Nanyang Technological University
|| Aug 2013
|| github link
|| Serge Hulne
|| Sep 2013
|| John Carty
|| Enlighten Jobs
|| Jan 2015
|| github link
|| Matías Guzmán Naranjo
|| May 2015
|| github link
|| Zalán Bodó
|| Babes-Bolyai University
|| Oct 2015
|| (Zalan’s notes)
|| Mohit Makkar
|| Indian Institute of Technology, Delhi
|| Nov 2015
All these encodings of the algorithm can be used free of charge for any purpose. Questions about the algorithms should be directed to their authors, and not to Martin Porter (except when he is the author).
To test the programs out, here is a sample vocabulary (0.19 megabytes), and the corresponding output.
Email any comments, suggestions, queries
Points of difference from the published algorithm
There is an extra rule in Step 2,
So archaeology is equated with archaeological etc.
- (m>0) logi → log
The Step 2 rule
is replaced by
- (m>0) abli → able
So possibly is equated with possible etc.
- (m>0) bli → ble
The algorithm leaves alone strings of length 1 or 2. In any case a string of length 1 will be unchanged if passed through the algorithm, but strings of length 2 might lose a final s, so as goes to a and is to i.
These differences may have been present in the program from which the published algorithm derived. But at such a great distance from the original publication it is now difficult to say.
It must be emphasised that these differences are very small indeed compared to the variations that have been observed in other encodings of the algorithm.
The Porter stemmer should be regarded as ‘frozen’, that is, strictly defined, and not amenable to further modification. As a stemmer, it is slightly inferior to the Snowball English or Porter2 stemmer, which derives from it, and which is subjected to occasional improvements. For practical work, therefore, the new Snowball stemmer is recommended. The Porter stemmer is appropriate to IR research work involving stemming where the experiments need to be exactly repeatable.
Historically, the following shortcomings have been found in other encodings of the stemming algorithm.
The algorithm clearly explains that when a set of rules of the type
are presented together, only one rule is applied, the one with the longest matching suffix S1 for the given word. This is true whether the rule succeeds or fails (i.e. whether or not S2 replaces S1). Despite this, the rules are sometimes simply applied in turn until either one of them succeeds or the list runs out.
- (condition)S1 → S2
This leads to small errors in various places, for example in the Step 4 rules
to remove final ement, ment and ent.
- (m>1)ement →
Properly, argument stems to argument. The longest matching suffix is -ment. Then stem argu- has measure m equal to 1 and so -ment will not be removed. End of Step 4. But if the three rules are applied in turn, then for suffix -ent the stem argum- has measure m equal to 2, and -ent gets removed.
The more delicate rules are liable to misinterpretation. (Perhaps greater care was required in explaining them.) So
is taken to mean
- ((m>1) and (*s or *t))ion
The former means that taking off -ion leaves a stem with measure greater than 1 ending -s or -t; the latter means that taking off -sion or -tion leaves a stem of measure greater than 1. A similar confusion tends to arise in interpreting rule 5b, to reduce final double L to single L.
- (m>1)(s or t)ion
Occasionally cruder errors have been seen. For example the test for Y being consonant or vowel set up the wrong way round.
It is interesting that although the published paper explains how to do the tests on the strings S1 by a program switch on the last or last but one letter, many encodings fail to use this technique, making them much slower than they need be.
FAQs (frequently asked questions)
#1. What is the licensing arrangement for this software?
This question has become very popular recently (the period 2008−2009), despite the clear statment above that ‘‘all these encodings of the algorithm can be used free of charge for any purpose.’’ The problem I think is that intellectual property has become such a major issue that some more formal statement is expected. So to restate it:
The software is completely free for any purpose, unless notes at the head of the program text indicates otherwise (which is rare). In any case, the notes about licensing are never more restrictive than the BSD License.
In every case where the software is not written by me (Martin Porter), this licensing arrangement has been endorsed by the contributor, and it is therefore unnecessary to ask the contributor again to confirm it.
I have not asked any contributors (or their employers, if they have them) for proofs that they have the right to distribute their software in this way.
(For anyone taking software from the Snowball website, the position is similar but simpler. There, all the software is issued under the BSD License, and for contributions not written by Martin Porter and Richard Boulton, we have again not asked the authors, or the authors’ employers, for proofs that they have such distribution rights.)
#2. Why is the stemmer not producing proper words?
It is often taken to be a crude error that a stemming algorithm does not leave a real word after removing the stem. But the purpose of stemming is to bring variant forms of a word together, not to map a word onto its ‘paradigm’ form.
And connected with this,
#3. Why are there errors?
The question normally comes in the form, why should word X be stemmed to x1, when one would have expected it to be stemmed to x2? It is important to remember that the stemming algorithm cannot achieve perfection. On balance it will (or may) improve IR performance, but in individual cases it may sometimes make what are, or what seem to be, errors. Of course, this is a different matter from suggesting an additional rule that might be included in the stemmer to improve its performance.