Transcript Topic Ontologies

CSE 535
Information Retrieval
Chapter 2: Text Operations
Srihari-CSE535-Spring2008
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
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Index Terms
Assigned terms
Extracted terms
1. Manual indexing
1. Extraction from title, abstract or
(a) Professional indexer
(b) Authors
full-text
2. Classification efforts (Library of
2. Methodology to extract must
Congress Classification, ACM)
distinguish significant from
(a) Coarseness: large collections?
non-significant terms
(b) Categories are mutually
3. Controlled (limit to pre-defined
exclusive
3. Subjectivity, inter-indexer
dictionary vs. non-controlled
consistency (:very low!)
vocabulary (all terms as they are)
4. Ontologies and classications:
4. Controlled ! less noise, better
limited appeal for retrieval
performance?
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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
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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
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Tokenization
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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?
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Parsing a document
 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 …
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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?
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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?
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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?
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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
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!
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Normalization
 “Right-to-left languages” like Hebrew and
Arabic: can have “left-to-right” text
interspersed.
 Need to “normalize” indexed
as7/30
well as
7月30text
日 vs.
query terms into the same form
 Character-level alphabet detection and
conversion


Tokenization not separable from this.
Sometimes ambiguous:
Morgen will ich in MIT …
Is this
German “mit”?
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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
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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.
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Case folding
 Reduce all letters to lower case

exception: upper case (in mid-sentence?)
 e.g., General Motors
 Fed vs. fed
 SAIL vs. sail
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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
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Assymetric query expansion
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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
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Dictionary entries – first cut
ensemble.french
時間.japanese
MIT.english
mit.german
guaranteed.english
entries.english
sometimes.english
These may be
grouped by
language. More
on this in
ranking/query
processing.
tokenization.english
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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 $term . ” \ n ” ;
}
}
}
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Building Lexical Analysers
 Finite state machines

recognizer for regular expressions
 Lex (http://www.combo.org/lex_yacc_page/lex.html)


popular Unix utility
program generator designed for lexical processing of character input
streams

input: high-level, problem oriented specification for character string
matching

output: program in a general purpose language which recognizes
regular expressions
+-------+
Source -> |
Lex
|
-> yylex
+-------+
+-------+
Input ->
| yylex | -> Output
+-------+
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Writing lex source files
Format of a lex source file:
{definitions}
%%
{rules}
%%
{user subroutines}
invoking lex:
lex source cc lex.yy.c -ll
•minimum lex program is %% (null program copies input to output)
•use rules to do any special transformations
Program to convert British spelling to American
colour printf("color");
mechanise printf("mechanize");
petrol printf("gas");
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Sample tokenizer using lex
letter
digit
id
number
%%
[a-zA-Z]
[0-9]
{letter}({letter}|{digit})*
{digit}+
printf("\nTokenizer running -- ^D to exit\n");
^{id}
{line();printf("<id>");}
{id}
printf("<id>");
^{number}
{line();printf("<number>");}
{number}
printf("<number>");
^[ \t]+
line();
[ \t]+
printf(" ");
[\n]
ECHO;
^[^a-zA-Z0-9 \t\n]+
{line();printf("\\%s\\",yytext);}
[^a-zA-Z0-9 \t\n]+
printf("\\%s\\",yytext);
%%
line()
{
printf("%4d: ",lineno++);
}
Sample input and output from above program
here are some ids
1: <id> <id> <id> <id>
now some numbers 123 4
2: <id> <id> <id> <number> <number>
ids with numbers x123 78xyz
3: <id> <id> <id> <id> <number><id>
garbage: @#$%^890abc
4: <id>\:\ \@#$%^\<number><id>
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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
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OAC Stopword list (~ 275 words)
The Complete OAC-Search Stopword List
a
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
Srihari-CSE535-Spring2008
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
sometimes
uses
using
yourself
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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
Srihari-CSE535-Spring2008
Stemming Algorithms

Designed to standardize morphological variations


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





stem is the portion of a word which is left after the removal of its affixes
(prefixes and suffixes)
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
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Stemming - example
 Word is “duplicatable”
 duplicat
 duplicate
 duplic
step 4
step 1b
step 3
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Stemming
 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 and
compres are both accept
as equival to compres.
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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.
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Typical rules in Porter
 sses  ss
 ies  i
 ational  ate
 tional  tion
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Other stemmers
 Other stemmers exist, e.g., Lovins stemmer
http://www.comp.lancs.ac.uk/computing/research/stemming/general/lovins.ht
m
 Single-pass, longest suffix removal (about 250
rules)
 Motivated by Linguistics as well as IR
 Full morphological analysis - modest benefits
for retrieval
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Stemming variants
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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.
Srihari-CSE535-Spring2008
Porter Stemmer - Types of Rules
The 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
(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’).
Srihari-CSE535-Spring2008
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
caress
->
caress
cats
->
cat
SS
->
S
->
SS
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Porter Stemmer- Step 1b
Step 1b
(m>0) EED
(*v*) ED
(*v*) ING
->
EE
->
->
feed
->
feed
agreed
->
agree
plastered
->
plaster
bled
->
bled
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
->
single letter
hopp(ing)
->
hop
tann(ed)
->
tan
fall(ing)
->
fall
hiss(ing)
->
hiss
fizz(ed)
->
fizz
fail(ing)
->
fail
fil(ing)
->
file
(*d and not (*L or *S or *Z))
(m=1 and *o)
->
E
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.
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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.
Srihari-CSE535-Spring2008
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.
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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
Srihari-CSE535-Spring2008
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.
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Porter Stemmer - Step 5
Step 5a
(m>1) E
(m=1 and not *o) E
->
probate
->
probat
rate
->
rate
->
cease
->
ceas
-> single letter
controll
->
control
roll
->
roll
Step 5b
(m > 1 and *d and *L)
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Thesauri
 Thesaurus



precompiled list of words in a given domain
for each word above, a list of related words
computer-aided instruction
computer applications
educational 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
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