python金融風控評分卡模型和數據分析微專業課（博主親自錄制視頻）：http://dwz.date/b9vv

最近需要對英文進行分詞處理，希望能夠實現還原英文單詞原型，比如 boys 變為 boy 等。

簡介

發現一個不錯的工具Porter stemmer，主頁是http://tartarus.org/~martin/PorterStemmer/。它被實現為N多版本，C、Java、Perl等。

下面是它的簡單介紹：

Stemming, in the parlance of searching and information retrieval, is the

operation of stripping the suffices from a word, leaving its stem.

Google, for instance, uses stemming to search for web pages containing

the words connected, connecting, connection and connections when

you ask for a web page that contains the word connect.

There are basically two ways to implement stemming. The first approach

is to create a big dictionary that maps words to their stems. The

advantage of this approach is that it works perfectly (insofar as the

stem of a word can be defined perfectly); the disadvantages

are the space required by the dictionary and the investment required to

maintain the dictionary as new words appear. The second approach is to

use a set of rules that extract stems from words. The advantages of this

approach are that the code is typically

small, and it can gracefully handle new words; the disadvantage is that

it occasionally makes mistakes. But, since stemming is imperfectly

defined, anyway, occasional mistakes are tolerable, and the rule-based

approach is the one that is generally chosen.

In 1979, Martin Porter developed a stemming algorithm that, with minor

modifications, is still in use today; it uses a set of rules to extract

stems from words, and though it makes some mistakes, most common words

seem to work out right. Porter describes his

algorithm and provides a reference implementation in C at http://tartarus.org/~martin/PorterStemmer/index.html;

以前也曾經嘗試過這個算法，但是因為下面的原因就放棄了！

比如輸入 "create" 和 "created" ，得到的結果是 "creat" 。這點讓我大失所望！這根本就沒有把單詞還原為原來的樣子啊？

這次沒辦法，還是需要實現這樣的功能，Google了半天，就發現Lucene里面有英文分詞模塊，可惜太復雜了，不適合我的這種簡單應用。后來才知道，其實lucene里用的也就是這種方法。

于是乎，硬著頭皮看了下他的主頁，在FQA里發現了下面這句話！恍然大悟。

The purpose of stemming is to bring variant forms of a word together, not to map a word onto its ‘paradigm’ form.

Porter stemmer

并不是要把單詞變為規范的那種原來的樣子，它只是把很多基于這個單詞的變種變為某一種形式！換句話說，它不能保證還原到單詞的原本，也就

是"created"不一定能還原到"create"，但卻可以使"create" 和 "created" ，都得到"creat" ！

實例

比如我輸入 "create" 和 "created" ，它解析得到 "creat"

那么，只需要在查詢時也做同樣的處理即可！比如查詢 "create created"，在數據庫里查的時候，都只需要檢索"creat"即可！

附錄

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.

History

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,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).With van Rijsbergen’s encouragement, it was also published in,M.F. Porter, 1980, An algorithm for suffix stripping, Program, 14(3) pp 130?137.And since then it has been reprinted inKaren Sparck Jones and Peter Willet, 1997, Readings in Information Retrieval, San Francisco: Morgan Kaufmann, ISBN 1-55860-454-4.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.

Encodings

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.

language

author

affiliation

received

notes

ANSI C thread safe

me

Daniel van Balen

Oct 1999

slightly faster?

The Official Web Guide

Sep 2001

Csharp .NET compliant

Univerity of Paisley, Scotland

Nov 2002

Csharp again!

Dec 2015

"more like standard

C# code" (Brad)

Brunel University

Apr 2003

Visual Basic

VB7; .NET compliant

University of Piraeus, Greece

Jan 2005

Richard Heyes

Feb 2005

University of Georgia

Oct 2005

Humboldt-Universitaet zu Berlin

May 2007

National Institute of Standards and

Technology, Gaithersburg, MD USA

Sep 2007

Mar 2013

bitbucket link

Zalán Bodó

Babes-Bolyai University

Oct 2015

Indian Institute of Technology, Delhi

Nov 2015

Dhaval Dave

Jun 2016

github link

P.O. Jonsson

Jul 2016

sourceforge link

Joey Takeda

Feb 2019

github link

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

Points of difference from the published algorithm

There is an extra rule in Step 2,(m>0) logi → logSo archaeology is equated with archaeological etc.

The Step 2 rule(m>0) abli → ableis replaced by(m>0) bli → bleSo possibly is equated with possible etc.

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.

Status

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.

Common errors

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(condition)S1 → S2are 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.

This leads to small errors in various places, for example in the Step 4 rules(m>1)ement →

(m>1)ment →

(m>1)ent →to remove final ement, ment and ent.

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((m>1) and (*s or *t))ionis taken to mean(m>1)(s or t)ionThe 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.

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.

python機器學習生物信息學系列課（博主錄制）：http://dwz.date/b9vw

歡迎關注博主主頁，學習python視頻資源

總結

以上是生活随笔為你收集整理的python 英语分词_英文分词算法(Porter stemmer)的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。