December 2007NLP: Conflation Algorithms1 Natural Language Processing Conflation Algorithms.

December 2007 NLP: Conflation Algorithms 1

Natural Language Processing

Conflation Algorithms


Acknowledgements

• John Repici (2002) http://www.creativyst.com/Doc/Articles/SoundEx1/SoundEx1.htm

• Porter, M.F., 1980, An algorithm for suffix stripping, reprinted in Sparck Jones, Karen, and Peter Willet, 1997, Readings in Information Retrieval, San Francisco: Morgan Kaufmann, ISBN 1-55860-454-4. [Vince has a copy of this]

• Jurafsky & Martin appendix B pp 833-836.


Conflation

COMPUTES

COMPUTE

COMPUTATIONCOMPUTABILITY

COMPUTING

COMPUTER

COMPUT


Word Conflation Algorithms

• Morphological analysis versus conflation

• Notion of word class is application dependent– Genealogy: Phonetic similarity– Information Retrieval: Semantic similarity

• Soundex

• Porter


Problems with Names

• Names can be misspelt: Rossner• Same name can be spelt in different ways

Kirkop; Chircop• Same name appears differently in different

cultures: Tchaikovsky; Chaicowski• To solve this problem, we need phonetically

oriented algorithms which can find similar sounding terms and names.

• Just such a family of algorithms exist and are called SoundExes, after the first patented version.


The Soundex Algorithm

• A Soundex algorithm takes a word as input and produces a character string which identifies a set of words that are (roughly) phonetically alike.

• It is very handy for searching large databases• Originally developed 1918 by Margaret K. Odell

and Robert C. Russell of the US Bureau of Archives, to simplify census-taking.


Soundex Algorithm 1

The Soundex Algorithm uses the following steps to encode a word:

1. The first character of the word is retained as the first character of the Soundex code.

2. The following letters are discarded: a,e,i,o,u,h,w, and y.

3. Remaining consonants are given a code number.4. If consonants having the same code number appear

consecutively, the number will only be coded once. (e.g. "B233" becomes "B23")


Code Numbers

b, p, f, and v 1

c, s, k, g, j, q, x, z 2

d, t 3

l 4

m,n 5

r 6


Soundex Algorithm: Example

The Soundex Algorithm uses the following steps to encode a word:

[ROSNER]1. The first character of the word is retained as the first

character of the Soundex code [R]2. The following letters are discarded: a,e,i,o,u,h,w, and

y. [RSNR]3. Remaining consonants are given a code number.

[R256]4. If consonants having the same code number appear

consecutively, the number will only be coded once. (e.g. "B233" becomes "B23")[R256]


Soundex Algorithm 2

– The resulting code is modified so that it becomes exactly four characters long: If it is less than 4 characters, zeroes are added to the end (e.g. "B2" becomes "B200")

– If it is more than 4 characters, the code is truncated (e.g. "B2435" becomes "B243")


Uses for the Soundex Code

• Airline reservations - The soundex code for a passenger's surname is often recorded to avoid confusion when trying to pronounce it.

• U.S. Census - As is noted above, the U.S. Census Department was a frequent user of the Soundex algorithm while trying to compile a listing of families around the turn of the century.

• Genealogy - In genealogy, the Soundex code is most often used to avoid obstacles when dealing with names that might have alternate spellings.


Improvements

• Preprocessing before applying the basic algorithm, e.g. identification of

– DG with G – GH with H – GN with N (not 'ng') – KN with N – PH with F

• Question: where to stop?

• Question: how to evaluate?


IR Applications

• Information Retrieval:

Query → → Relevant Documents

• “Bag of Terms” document model

• What is a single term?


Why Stemming is Necessary

• Frequently we get collections of words of the following kind in the same document

compute, computer, computing, computation, computability ….

• Performance of IR system will be improved if all of these terms are conflated.– Less terms to worry about– More accurate statistics


Issues

• Is a dictionary available?– Stems– Affixes

• Motivation: linguistic credibility or engineering performance?

• When to remove a affix versus when to leave it alone

• Porter (1980): W1 and W2 should be conflated if there appears to be no difference between the statements "this document is about W1/W2"

relate/relativity vs. radioactive/radioactivity


Consonants and Vowels

• A consonant is a letter other than a,e,i,o,u and other than y preceded by a consonant: sky, toy

• If a letter is not a consonant it is a vowel.• A sequence of consonants (cc..c) or vowels (vv..v) will

be represented by C or V respectively.• For example the word troubles maps to C V C V C• Any word or part of a word, therefore has one of the

following forms:

(CV)n….C(CV)n….V(VC)n….C(VC)n….V


Measure

• All the above patterns can be replaced bythe following regular expression

(C) (VC)m (V)

• m is called the measure of any word or word part.

• m=0: tr, ee, tree, y, bym=1: trouble, oats, trees, ivym=2: troubles; private


Rules

• Rules for removing a suffix are given in the form

(condition) S1 → S2

• i.e. if a word ends with suffix S1, and the stem before S1 satisfies the condition, then it is replaced with S2. Example

(m > 1) EMENT →

• Example: enlargement → enlarg


Conditions

• *S - stem ends with s• *Z - stem ends with z• *T – stem ends with t• *v* - stem contains a vowel• *d - stem ends with a double consonant• *o - stem ends cvc, where second c is not w, x

or y e.g. –wil, -hop• In conditions, Boolean operators are possible

e.g. (m>1 and (*S or *T))• Sets of rules applied in 7 steps. Within each

step, rule matching longest suffix applies.


OrganisationStep 1Plurals and Third Person Singular Verbs

Step 2Verbal Past Tense and Progressive

Step 3: Y to INoun Inflections

Steps 4 and 5Derivational MorphologyMultiple Suffixesvisualisation → visualise

Steps 6Derivational MorphologySingle Suffixes

Step 7Cleanup

-s

-ed, -ing fly/flies


Step 1:Plural Nouns and 3rd Person Singular Verbs

condition rewrite example

SSES → SS caresses → caress

IES → I ponies → poni

SS → SS caress → caress

S → cats → cat


Step 2a Verbal Past Tense and Progressive Forms


(m>0) EED → EE feed → feed

agreed → agree

(*v*) ED → ε plastered → plaster

bled → bled

(*v*) ING → ε killing → killsing → sing


Step 2b: CleanupIf 2nd or 3rd of last step succeeds


AT → ATE generat → generate

BL → BLE troubl → trouble

IZ → IZE capsiz → capsize

*d and not

(*L or *S or *Z)

→

single letter

hopp → hop

hiss → hiss


Step 3: Y to I

(*v*) Y → I happy → happi

cry → cry


STEP 4: Derivational Morphology 1 – Multiple Suffixes (excerpt)

Condition Rewrite Example

(m > 0) ATIONAL → ATE relational → relate

(m > 0) TIONAL → TION conditional → condition

(m > 0) ENCI → ENCE valenci → valence

(m > 0) ABLI → ABLE comfortabli → comfortable

(m > 0) OUSLI → OUS analagously → analagous

(m > 0) IZATION → IZE digitizer → digitize

(m > 0) ATION → ATE generation → generate

(m > 0) ATOR → ATE operator → operate

(m > 0) ALISM → AL formalism → formal

(m > 0) IVENESS → IVE pensiveness → pensive

(m > 0) FULNESS → FUL hopefulness → hopeful

(m > 0) OUSNESS → OUS callousness → callous

(m > 0) ALITI → AL formality → formal

(m > 0) BILITI → BLE possibility → possible

December 2007 26

Step 6: Derivational Morphology III: Single Suffixes

Condition Rewrite Example

(m > 1) AL → ε revival → reviv

(m > 1) ANCE → ε allowance → allow

(m > 1) ENCE → ε inference → infer

(m > 1) ER → ε airliner → airlin

(m > 1) IC → ε Coptic → Copt

(m > 1) ABLE → ε laughable → laugh

(m > 1) ANT → ε irritant → irrit

(m > 1) EMENT → ε replacement → replac

(m > 1) MENT → ε adjustment → adjust

(m > 1) ENT → ε dependent → depend

(m > 0) (*S or *T) ION → ε adoption → adopt

(m > 1) OU → ε callousness → callous

(m > 1) ISM → ε formalism→ formal

(m > 1) ATE → ε activate → activ

ITI → ε


Porter Example

• INPUTin the first focus area, integrated projects shall help develop, principally, common open platforms for software and services supporting a distributed information and decision systems for risk and crisis management


Porter Output

Original Word Stemmed Word

first first

focus focu

area area

integrated integr

projects project

help help

develop develop

principally princip

common common

open open

platforms platform

Original Word Stemmed Word

platforms platform

software softwar

services servic

supporting support

distributed distribut

information inform

decision decis

systems system

risk risk

crisis crisi

management manag


Summary

• Conflation serves different purposes

• Generally, motivation is to achieve an engineering goal rather than linguistic fidelity.

• This can cause errors in the bag of words model.

• Soundex and Porter very well established and easily available.

December 2007NLP: Conflation Algorithms1 Natural Language Processing Conflation Algorithms.

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