srihari-cse535-spring2008 cse 535 information retrieval chapter 2: text operations

Srihari-CSE535-Spring2008

CSE 535Information Retrieval

Chapter 2: Text OperationsChapter 2: Text Operations


Logical View

1. Description of content and semantics of document

2. Structure

3. Derivation of index terms:(a) removal of stopwords(b) stemming(c) identification of nouns

1. Description of content and semantics of document

2. Structure

3. Derivation of index terms:(a) removal of stopwords(b) stemming(c) identification of nouns


Index Terms

Assigned terms

1. Manual indexing

(a) Professional indexer

(b) Authors

2. Classification efforts (Library of

Congress Classification, ACM)

(a) Coarseness: large collections?

(b) Categories are mutually

exclusive

3. Subjectivity, inter-indexer

consistency (:very low!)

4. Ontologies and classications:

limited appeal for retrieval

Assigned terms

1. Manual indexing

(a) Professional indexer

(b) Authors

2. Classification efforts (Library of

Congress Classification, ACM)

(a) Coarseness: large collections?

(b) Categories are mutually

exclusive

3. Subjectivity, inter-indexer

consistency (:very low!)

4. Ontologies and classications:

limited appeal for retrieval

Extracted terms

1. Extraction from title, abstract or

full-text

2. Methodology to extract must

distinguish significant from

non-significant terms

3. Controlled (limit to pre-defined

dictionary vs. non-controlled

vocabulary (all terms as they are)

4. Controlled ! less noise, better

performance?

Extracted terms

1. Extraction from title, abstract or

full-text

2. Methodology to extract must

distinguish significant from

non-significant terms

3. Controlled (limit to pre-defined

dictionary vs. non-controlled

vocabulary (all terms as they are)

4. Controlled ! less noise, better

performance?


Document Pre-Processing

Lexical analysis digits, hypens, punctuation marks e.g. I’ll --> I will

Stopword elimination filter out words with low discriminatory value stopword list reduces size of index by 40% or more might miss documents, e.g. “to be or not to be”

Stemming connecting, connected, etc.

Selection of index terms e.g. select nouns only

Thesaurus construction permits query expansion

Lexical analysis digits, hypens, punctuation marks e.g. I’ll --> I will

Stopword elimination filter out words with low discriminatory value stopword list reduces size of index by 40% or more might miss documents, e.g. “to be or not to be”

Stemming connecting, connected, etc.

Selection of index terms e.g. select nouns only

Thesaurus construction permits query expansion


Lexical Analysis

Converting stream of characters into stream of words/tokens

more than just detecting spaces between words (see next slide)

Disregard numbers as index terms too vague perform date and number normalization

Hyphens consistent policy on removal e.g. B-52

Punctuation marks typically removed sometimes problematic, e.g. variable names

myclass.height

Orthographic variations typically disregarded

Converting stream of characters into stream of words/tokens

more than just detecting spaces between words (see next slide)

Disregard numbers as index terms too vague perform date and number normalization

Hyphens consistent policy on removal e.g. B-52

Punctuation marks typically removed sometimes problematic, e.g. variable names

myclass.height

Orthographic variations typically disregarded


Tokenization


Tokenization

Input: “Friends, Romans and Countrymen”

Output: Tokens Friends Romans Countrymen

Each such token is now a candidate for an index entry, after further processing

Described below

But what are valid tokens to emit?

Input: “Friends, Romans and Countrymen”

Output: Tokens Friends Romans Countrymen

Each such token is now a candidate for an index entry, after further processing

Described below

But what are valid tokens to emit?


Parsing a document

What format is it in? pdf/word/excel/html?

What language is it in?

What character set is in use?

What format is it in? pdf/word/excel/html?

What language is it in?

What character set is in use?

Each of these is a classification problem.

But there are complications …


Format/language stripping

Documents being indexed can include docs from many different languages

A single index may have to contain terms of several languages.

Sometimes a document or its components can contain multiple languages/formats

French email with a Portuguese pdf attachment.

What is a unit document? An email? With attachments? An email with a zip containing documents?

Documents being indexed can include docs from many different languages

A single index may have to contain terms of several languages.

Sometimes a document or its components can contain multiple languages/formats

French email with a Portuguese pdf attachment.

What is a unit document? An email? With attachments? An email with a zip containing documents?


Tokenization

Issues in tokenization: Finland’s capital Finland? Finlands?

Finland’s? Hewlett-Packard Hewlett and Packard as two

tokens? San Francisco: one token or two? How do you

decide it is one token?

Issues in tokenization: Finland’s capital Finland? Finlands?

Finland’s? Hewlett-Packard Hewlett and Packard as two

tokens? San Francisco: one token or two? How do you

decide it is one token?


Language issues

Accents: résumé vs. resume.

L'ensemble one token or two? L ? L’ ? Le ?

How are your users like to write their queries for these words?

Accents: résumé vs. resume.

L'ensemble one token or two? L ? L’ ? Le ?

How are your users like to write their queries for these words?


Tokenization: language issues

Chinese and Japanese have no spaces between words:

Not always guaranteed a unique tokenization

Further complicated in Japanese, with multiple alphabets intermingled

Dates/amounts in multiple formats

Chinese and Japanese have no spaces between words:

Not always guaranteed a unique tokenization

Further complicated in Japanese, with multiple alphabets intermingled

Dates/amounts in multiple formats

フォーチュン 500社は情報不足のため時間あた $500K(約 6,000万円 )

Katakana Hiragana Kanji “Romaji”

End-user can express query entirely in (say) Hiragana!


Normalization

“Right-to-left languages” like Hebrew and Arabic: can have “left-to-right” text interspersed.

Need to “normalize” indexed text as well as query terms into the same form

Character-level alphabet detection and conversion

Tokenization not separable from this. Sometimes ambiguous:

“Right-to-left languages” like Hebrew and Arabic: can have “left-to-right” text interspersed.

Need to “normalize” indexed text as well as query terms into the same form

Character-level alphabet detection and conversion

Tokenization not separable from this. Sometimes ambiguous:

7 月 30 日 vs. 7/30

Morgen will ich in MIT …

Is thisGerman “mit”?


Punctuation

Ne’er: use language-specific, handcrafted “locale” to normalize.

Which language? Most common: detect/apply language at a pre-

determined granularity: doc/paragraph.

State-of-the-art: break up hyphenated sequence. Phrase index?

U.S.A. vs. USA - use locale.

a.out

Ne’er: use language-specific, handcrafted “locale” to normalize.

Which language? Most common: detect/apply language at a pre-

determined granularity: doc/paragraph.

State-of-the-art: break up hyphenated sequence. Phrase index?

U.S.A. vs. USA - use locale.

a.out


Numbers

3/12/91

Mar. 12, 1991

55 B.C.

B-52

My PGP key is 324a3df234cb23e

100.2.86.144 Generally, don’t index as text. Will often index “meta-data” separately

Creation date, format, etc.

3/12/91

Mar. 12, 1991

55 B.C.

B-52

My PGP key is 324a3df234cb23e

100.2.86.144 Generally, don’t index as text. Will often index “meta-data” separately

Creation date, format, etc.


Case folding

Reduce all letters to lower case exception: upper case (in mid-sentence?)

e.g., General Motors Fed vs. fed SAIL vs. sail

Reduce all letters to lower case exception: upper case (in mid-sentence?)

e.g., General Motors Fed vs. fed SAIL vs. sail


Thesauri and soundex

Handle synonyms and homonyms Hand-constructed equivalence classes

e.g., car = automobile your and you’re

Index such equivalences When the document contains automobile, index it under

car as well (usually, also vice-versa)

Or expand query? When the query contains automobile, look under car as

well

Handle synonyms and homonyms Hand-constructed equivalence classes

e.g., car = automobile your and you’re

Index such equivalences When the document contains automobile, index it under

car as well (usually, also vice-versa)

Or expand query? When the query contains automobile, look under car as

well


Assymetric query expansion


Lemmatization

Reduce inflectional/variant forms to base form

E.g., am, are, is be car, cars, car's, cars' car

the boy's cars are different colors the boy car be different color

Reduce inflectional/variant forms to base form

E.g., am, are, is be car, cars, car's, cars' car

the boy's cars are different colors the boy car be different color


Dictionary entries – first cut

ensemble.french

時間 .japanese

MIT.english

mit.german

guaranteed.english

entries.english

sometimes.english

tokenization.english

These may be grouped by language. More on this

in ranking/query processing.


Perl program to Split Words

# ! / usr/bin/perl

while (<>)

{ $ line = $ ;

chomp ( $ line ) ; Store line (with CR removed)

if ( $ line = ˜ / [ aÅ|z ] / )

{ # split on spaces

@words = split / \ s + / , $line ; words is an associative array

# separately print all words in line

for each $term ( @words )

{ # lowercase

$term = ˜ tr / [A-Z ] / [ a-z ] / ;

# remove punctuation

$term = ˜ s / \ , | \ . | \ ; | \ : | \ ” | \ ’ | \ ‘ | \ ? | \ ! / / g ;

print $term . ” \ n ” ;

}

}

}

# ! / usr/bin/perl

while (<>)

{ $ line = $ ;

chomp ( $ line ) ; Store line (with CR removed)

if ( $ line = ˜ / [ aÅ|z ] / )

{ # split on spaces

@words = split / \ s + / , $line ; words is an associative array

# separately print all words in line

for each $term ( @words )

{ # lowercase

$term = ˜ tr / [A-Z ] / [ a-z ] / ;

# remove punctuation

$term = ˜ s / \ , | \ . | \ ; | \ : | \ ” | \ ’ | \ ‘ | \ ? | \ ! / / g ;

print $term . ” \ n ” ;

}

}

}


Building Lexical Analysers Finite state machines

recognizer for regular expressions

Lex (http://www.combo.org/lex_yacc_page/lex.html) popular Unix utility program generator designed for lexical processing of

character input streams input: high-level, problem oriented specification for

character string matching output: program in a general purpose language which

recognizes regular expressions

+-------+

Source -> | Lex | -> yylex

+-------+

+-------+

Input -> | yylex | -> Output

+-------+

Finite state machines recognizer for regular expressions

Lex (http://www.combo.org/lex_yacc_page/lex.html) popular Unix utility program generator designed for lexical processing of

character input streams input: high-level, problem oriented specification for

character string matching output: program in a general purpose language which

recognizes regular expressions

+-------+

Source -> | Lex | -> yylex

+-------+

+-------+

Input -> | yylex | -> Output

+-------+


Writing lex source files

Format of a lex source file:

{definitions}

%%

{rules}

%%

{user subroutines}

invoking lex:

lex source cc lex.yy.c -ll

•minimum lex program is %% (null program copies input to output)

•use rules to do any special transformations

Program to convert British spelling to American

colour printf("color");

mechanise printf("mechanize");

petrol printf("gas");


Sample tokenizer using lexletter [a-zA-Z]digit [0-9]id {letter}({letter}|{digit})*number {digit}+%%

printf("\nTokenizer running -- ^D to exit\n");

^{id} {line();printf("<id>");}{id} printf("<id>");^{number} {line();printf("<number>");}{number} printf("<number>");^[ \t]+ line();[ \t]+ printf(" ");[\n] ECHO;^[^a-zA-Z0-9 \t\n]+ {line();printf("\\%s\\",yytext);}[^a-zA-Z0-9 \t\n]+ printf("\\%s\\",yytext);%%line(){

printf("%4d: ",lineno++);}

Sample input and output from above programhere are some ids 1: <id> <id> <id> <id>now some numbers 123 4 2: <id> <id> <id> <number> <number>ids with numbers x123 78xyz 3: <id> <id> <id> <id> <number><id>garbage: @#$%^890abc 4: <id>\:\ \@#$%^\<number><id>


Stopword Elimination

1. Stopwords:

(a) Too frequent, P(ki) > 0.8

(b) Irrelevant in linguistic terms

i. Do not correpond to nouns, concepts etc

ii. Could however be vital in natural language processing

2. Removal from collection on two grounds:

(a) Retrieval efficiency

i. High frequency: no distinction between documents

ii. Useless for retrieval

(b) Storage efficiency

i. Zipf’s law

ii. Heap’s law

1. Stopwords:

(a) Too frequent, P(ki) > 0.8

(b) Irrelevant in linguistic terms

i. Do not correpond to nouns, concepts etc

ii. Could however be vital in natural language processing

2. Removal from collection on two grounds:

(a) Retrieval efficiency

i. High frequency: no distinction between documents

ii. Useless for retrieval

(b) Storage efficiency

i. Zipf’s law

ii. Heap’s law


OAC Stopword list (~ 275 words)

The Complete OAC-Search Stopword Lista did has nobody somewhere usually

about do have noone soon very

all does having nor still was

almost doing here not such way

along done how nothing than ways

already during however now that we

also each i of the well

although either if once their went

always enough ii one theirs were

am etc in one's then what

among even including only there when

an ever indeed or therefore whenever

and every instead other these where

any everyone into others they wherever

anyone everything is our thing whether

anything for it ours things which


Stopword list contd.

anywhere from its out this while

are further itself over those who

as gave just own though whoever

at get like perhaps through why

away getting likely rather thus will

because give may really to with

between given maybe same together within

beyond giving me shall too without

both go might should toward would

but going mine simply until yes

by gone much since upon yet

can good must so us you

cannot got neither some use your

come great never someone used yours

could had no something uses yourself sometimes using


Implementing Stoplists

2 ways to filter stoplist words from input token stream

examine tokenizer output and remove any stopwords remove stopwords as part of lexical analysis (or

tokenization) phase

Approach 1: filtering after tokenization standard list search problem; every token must be looked

up in stoplist binary search trees, hashing hashing is preferred method

insert stoplist into a hash table each token hashed into table if location is empty, not a stopword; otherwise

string comparisons to see whether hashed value is really a stopword

Approach 2 much more efficient

2 ways to filter stoplist words from input token stream

examine tokenizer output and remove any stopwords remove stopwords as part of lexical analysis (or

tokenization) phase

Approach 1: filtering after tokenization standard list search problem; every token must be looked

up in stoplist binary search trees, hashing hashing is preferred method

insert stoplist into a hash table each token hashed into table if location is empty, not a stopword; otherwise

string comparisons to see whether hashed value is really a stopword

Approach 2 much more efficient


Stemming Algorithms

Designed to standardize morphological variations stem is the portion of a word which is left after the

removal of its affixes (prefixes and suffixes)

Benefits of stemming supposedly, to increase recall; could lead to lower

precision controversy surrounding benefits web search engines do NOT perform stemming Reduces size of index (clear benefit)

4 types of stemming algorithms affix removal: most commonly used table look-up: considerable storage space, all data not

available successor variety : (uses knowledge from structural

linguistics, more complex) n-grams; more a term clustering procedure that a stemming

algorithm

Porter Stemming algorithm http://snowball.sourceforge.net/porter/stemmer.html

Designed to standardize morphological variations stem is the portion of a word which is left after the

removal of its affixes (prefixes and suffixes)

Benefits of stemming supposedly, to increase recall; could lead to lower

precision controversy surrounding benefits web search engines do NOT perform stemming Reduces size of index (clear benefit)

4 types of stemming algorithms affix removal: most commonly used table look-up: considerable storage space, all data not

available successor variety : (uses knowledge from structural

linguistics, more complex) n-grams; more a term clustering procedure that a stemming

algorithm

Porter Stemming algorithm http://snowball.sourceforge.net/porter/stemmer.html


Stemming - example

Word is “duplicatable”

duplicat step 4

duplicate step 1b

duplic step 3

Word is “duplicatable”

duplicat step 4

duplicate step 1b

duplic step 3


Stemming

Reduce terms to their “roots” before indexing

language dependent e.g., automate(s), automatic, automation all reduced

to automat.

Reduce terms to their “roots” before indexing

language dependent e.g., automate(s), automatic, automation all reduced

to automat.

for example compressed and compression are both accepted as equivalent to compress.

for exampl compres andcompres are both acceptas equival to compres.


Porter’s algorithm

Commonest algorithm for stemming English

Conventions + 5 phases of reductions phases applied sequentially each phase consists of a set of commands sample convention: Of the rules in a compound

command, select the one that applies to the longest suffix.

Commonest algorithm for stemming English

Conventions + 5 phases of reductions phases applied sequentially each phase consists of a set of commands sample convention: Of the rules in a compound

command, select the one that applies to the longest suffix.


Typical rules in Porter

sses ss

ies i

ational ate

tional tion

sses ss

ies i

ational ate

tional tion


Other stemmers

Other stemmers exist, e.g., Lovins stemmer http://www.comp.lancs.ac.uk/computing/research/stemming/general/lovins.htm

Single-pass, longest suffix removal (about 250 rules)

Motivated by Linguistics as well as IR

Full morphological analysis - modest benefits for retrieval

Other stemmers exist, e.g., Lovins stemmer http://www.comp.lancs.ac.uk/computing/research/stemming/general/lovins.htm

Single-pass, longest suffix removal (about 250 rules)

Motivated by Linguistics as well as IR

Full morphological analysis - modest benefits for retrieval


Stemming variants


Porter Stemmer : Representing Strings

A consonant in a word is a letter other than A, E, I, O or U, and other than Y preceded by a consonant.

(The fact that the term ‘consonant’ is defined to some extent in terms of itself does not make it ambiguous.)

So in TOY the consonants are T and Y, and in SYZYGY they are S, Z and G.

If a letter is not a consonant it is a vowel.

A consonant will be denoted by c, a vowel by v. A list ccc... of length greater than 0 will be denoted by C,

and a list vvv... of length greater than 0 will be denoted by V. Any word, or part of a word,

therefore has one of the four forms:

CVCV ... C CVCV ... V VCVC ... C VCVC ... V

These may all be represented by the single form

[C]VCVC ... [V]

where the square brackets denote arbitrary presence of their contents. Using (VC)m to denote VC repeated m times,

this may again be written as

[C](VC)m[V].

m will be called the measure of any word or word part when represented in this form. The case m = 0 covers the null word.

Here are some examples:

m=0 TR, EE, TREE, Y, BY.

m=1 TROUBLE, OATS, TREES, IVY.

m=2 TROUBLES, PRIVATE, OATEN, ORRERY.


Porter Stemmer - Types of RulesThe rules for removing a suffix will be given in the form

(condition) S1 -> S2

This means that if a word ends with the suffix S1, and the stem before S1 satisfies the given condition, S1 is replaced by S2. The condition is usually given in terms of m, e.g.

(m > 1) EMENT ->

Here S1 is ‘EMENT’ and S2 is null. This would map REPLACEMENT to REPLAC, since REPLAC is a word part for which m = 2.

The ‘condition’ part may also contain the following:

*S - the stem ends with S (and similarly for the other letters).

*v* - the stem contains a vowel.

*d - the stem ends with a double consonant (e.g. -TT, -SS).

*o - the stem ends cvc, where the second c is not W, X or Y (e.g. -WIL, -HOP).

And the condition part may also contain expressions with and, or and not, so that

(m>1 and (*S or *T))

tests for a stem with m>1 ending in S or T, while

(*d and not (*L or *S or *Z))

tests for a stem ending with a double consonant other than L, S or Z. Elaborate conditions like this are required only rarely.

In a set of rules written beneath each other, only one is obeyed, and this will be the one with the longest matching S1 for the given word. For example, with

SSES -> SS

IES -> I

SS -> SS

S ->

(here the conditions are all null) CARESSES maps to CARESS since SSES is the longest match for S1.

Equally CARESS maps to CARESS (S1=‘SS’) and CARES to CARE (S1=‘S’).


Porter Stemmer - Step 1a

In the rules below, examples of their application, successful or otherwise, are given

on the right in lower case. Only one rule from each step can fire. The algorithm now follows:

Step 1a

SSES -> SS caresses -> caress

IES -> I ponies -> poni

ties -> ti

SS -> SS caress -> caress

S -> cats -> cat


Porter Stemmer- Step 1bStep 1b

(m>0) EED -> EE feed -> feed

agreed -> agree

(*v*) ED -> plastered -> plaster

bled -> bled

(*v*) ING -> motoring -> motor

sing -> sing

If the second or third of the rules in Step 1b is successful, the following is done:

AT -> ATE conflat(ed) -> conflate

BL -> BLE troubl(ed) -> trouble

IZ -> IZE siz(ed) -> size

(*d and not (*L or *S or *Z)) -> single letter hopp(ing) -> hop

tann(ed) -> tan

fall(ing) -> fall

hiss(ing) -> hiss

fizz(ed) -> fizz

(m=1 and *o) -> E fail(ing) -> fail

fil(ing) -> file

The rule to map to a single letter causes the removal of one of the double letter pair. The -E is put back on -AT, -BL and -IZ, so that the suffixes

-ATE, -BLE and -IZE can be recognised later. This E may be removed in step 4.


Porter Stemmer- Step 1c

Step 1c

(*v*) Y -> I happy -> happi

sky -> sky

Step 1 deals with plurals and past participles. The subsequent steps are

much more straightforward.


Porter Stemmer - Step 2(m>0) ATIONAL -> ATE relational -> relate

(m>0) TIONAL -> TION conditional -> condition

rational -> rational

(m>0) ENCI -> ENCE valenci -> valence

(m>0) ANCI -> ANCE hesitanci -> hesitance

(m>0) IZER -> IZE digitizer -> digitize

(m>0) ABLI -> ABLE conformabli -> conformable

(m>0) ALLI -> AL radicalli -> radical

(m>0) ENTLI -> ENT differentli -> different

(m>0) ELI -> E vileli -> vile

(m>0) OUSLI -> OUS analogousli -> analogous

(m>0) IZATION -> IZE vietnamization -> vietnamize

(m>0) ATION -> ATE predication -> predicate

(m>0) ATOR -> ATE operator -> operate

(m>0) ALISM -> AL feudalism -> feudal

(m>0) IVENESS -> IVE decisiveness -> decisive

(m>0) FULNESS -> FUL hopefulness -> hopeful

(m>0) OUSNESS -> OUS callousness -> callous

(m>0) ALITI -> AL formaliti -> formal

(m>0) IVITI -> IVE sensitiviti -> sensitive

(m>0) BILITI -> BLE sensibiliti -> sensible

The test for the string S1 can be made fast by doing a program switch on the penultimate letter of the word being tested. This gives a fairly even breakdown

of the possible values of the string S1. It will be seen in fact that the S1-strings in step 2 are presented here in the alphabetical order of their penultimate letter.

Similar techniques may be applied in the other steps.


Porter Stemmer - Step 3

(m>0) ICATE -> IC triplicate -> triplic

(m>0) ATIVE -> formative -> form

(m>0) ALIZE -> AL formalize -> formal

(m>0) ICITI -> IC electriciti -> electric

(m>0) ICAL -> IC electrical -> electric

(m>0) FUL -> hopeful -> hope

(m>0) NESS -> goodness -> good


Porter Stemmer- Step 4(m>1) AL -> revival -> reviv

(m>1) ANCE -> allowance -> allow

(m>1) ENCE -> inference -> infer

(m>1) ER -> airliner -> airlin

(m>1) IC -> gyroscopic -> gyroscop

(m>1) ABLE -> adjustable -> adjust

(m>1) IBLE -> defensible -> defens

(m>1) ANT -> irritant -> irrit

(m>1) EMENT -> replacement -> replac

(m>1) MENT -> adjustment -> adjust

(m>1) ENT -> dependent -> depend

(m>1 and (*S or *T)) ION -> adoption -> adopt

(m>1) OU -> homologou -> homolog

(m>1) ISM -> communism -> commun

(m>1) ATE -> activate -> activ

(m>1) ITI -> angulariti -> angular

(m>1) OUS -> homologous -> homolog

(m>1) IVE -> effective -> effect

(m>1) IZE -> bowdlerize -> bowdler

The suffixes are now removed. All that remains is a little tidying up.


Porter Stemmer - Step 5

Step 5a

(m>1) E -> probate -> probat

rate -> rate

(m=1 and not *o) E -> cease -> ceas

Step 5b

(m > 1 and *d and *L) -> single letter controll -> control

roll -> roll


Thesauri Thesaurus

precompiled list of words in a given domain for each word above, a list of related words computer-aided instruction

computer applicationseducational computing

Applications of Thesaurus in IR provides a standard vocabulary for indexing and

searching medical literature querying proper query formulation classified hierarchies that allow

broadening/narrowing of current query e.g. Yahoo

Components of a thesaurus index terms relationship among terms layout design for term relationships

Thesaurus precompiled list of words in a given domain for each word above, a list of related words computer-aided instruction

computer applicationseducational computing

Applications of Thesaurus in IR provides a standard vocabulary for indexing and

searching medical literature querying proper query formulation classified hierarchies that allow

broadening/narrowing of current query e.g. Yahoo

Components of a thesaurus index terms relationship among terms layout design for term relationships