Lecture 4: Boolean IR System Elements

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Prof. Ray Larson University of California, Berkeley

School of Information

Principles of Information Retrieval

Lecture 4: Boolean IR System Elements

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Review• Review

– Elements of IR Systems• Collections, Queries• Text processing and Zipf distribution

– Stemmers and Morphological analysis• Inverted file indexes • IR Models - Introduction to the Boolean

Model

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Queries• A query is some expression of a user’s

information needs• Can take many forms

– Natural language description of need– Formal query in a query language

• Queries may not be accurate expressions of the information need– Differences between conversation with a

person and formal query expression

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Collections of Documents…• Documents

– A document is a representation of some aggregation of information, treated as a unit.

• Collection– A collection is some physical or logical

aggregation of documents• Let’s take the simplest case, and say we

are dealing with a computer file of plain ASCII text, where each line represents the “UNIT” or document.

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How to search that collection?• Manually?

– Cat, more• Scan for strings?

– Grep• Extract individual words to search???

– “tokenize” (a unix pipeline)• tr -sc ’A-Za-z’ ’\012’ < TEXTFILE | sort | uniq –c

– See “Unix for Poets” by Ken Church

• Put it in a DBMS and use pattern matching there…– assuming the lines are smaller than the text size limits

for the DBMS

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What about VERY big files?• Scanning becomes a problem• The nature of the problem starts to change

as the scale of the collection increases• A variant of Parkinson’s Law that applies

to databases is:– Data expands to fill the space available to

store it

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Document Processing Steps

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Structure of an IR SystemSearchLine Interest profiles

& QueriesDocuments

& data

Rules of the game =Rules for subject indexing +

Thesaurus (which consists of

Lead-InVocabulary

andIndexing

Language

StorageLine

Potentially Relevant

Documents

Comparison/Matching

Store1: Profiles/Search requests

Store2: Documentrepresentations

Indexing (Descriptive and

Subject)

Formulating query in terms of

descriptors

Storage of profiles Storage of

Documents

Information Storage and Retrieval System

Adapted from Soergel, p. 19

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Query Processing• In order to correctly match queries and

documents they must go through the same text processing steps as the documents did when they were stored

• In effect, the query is treated like it was a document

• Exceptions (of course) include things like structured query languages that must be parsed to extract the search terms and requested operations from the query– The search terms must still go through the same text

process steps as the document…

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Steps in Query processing• Parsing and analysis of the query text

(same as done for the document text)– Morphological Analysis– Statistical Analysis of text

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Plotting Word Frequency by Rank

• Say for a text with 100 tokens• Count

– How many tokens occur 1 time (50)– How many tokens occur 2 times (20) …– How many tokens occur 7 times (10) … – How many tokens occur 12 times (1)– How many tokens occur 14 times (1)

• So things that occur the most often share the highest rank (rank 1).

• Things that occur the fewest times have the lowest rank (rank n).

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Many similar distributions…• Words in a text collection• Library book checkout patterns• Bradford’s and Lotka’s laws.• Incoming Web Page Requests (Nielsen)• Outgoing Web Page Requests (Cunha &

Crovella)• Document Size on Web (Cunha &

Crovella)

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Zipf Distribution(linear and log scale)

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Resolving Power (van Rijsbergen 79)The most frequent words are not the most descriptive.

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Other Models• Poisson distribution• 2-Poisson Model• Negative Binomial• Katz K-mixture

– See Church (SIGIR 1995)

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Stemming and Morphological Analysis

• Goal: “normalize” similar words• Morphology (“form” of words)

– Inflectional Morphology• E.g,. inflect verb endings and noun number• Never change grammatical class

– dog, dogs– tengo, tienes, tiene, tenemos, tienen

– Derivational Morphology • Derive one word from another, • Often change grammatical class

– build, building; health, healthy

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Stemming and Morphological Analysis• Goal: “normalize” similar words• Morphology (“form” of words)

– Inflectional Morphology• E.g,. inflect verb endings and noun number• Never change grammatical class

– dog, dogs– tengo, tienes, tiene, tenemos, tienen

– Derivational Morphology • Derive one word from another, • Often change grammatical class

– build, building; health, healthy

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Simple “S” stemming• IF a word ends in “ies”, but not “eies” or

“aies”– THEN “ies” “y”

• IF a word ends in “es”, but not “aes”, “ees”, or “oes”– THEN “es” “e”

• IF a word ends in “s”, but not “us” or “ss”– THEN “s” NULL

Harman, JASIS Jan. 1991

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Too Aggressive Too Timid

organization/organpolicy/police

execute/executivearm/army

european/europecylinder/cylindrical

create/creationsearch/searcher

Errors Generated by Porter Stemmer (Krovetz 93)

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Automated Methods• Stemmers:

– Very dumb rules work well (for English)– Porter Stemmer: Iteratively remove suffixes– Improvement: pass results through a lexicon

• Newer stemmers are configurable (Snowball)– Demo…

• Powerful multilingual tools exist for morphological analysis– PCKimmo, Xerox Lexical technology– Require a grammar and dictionary– Use “two-level” automata– Wordnet “morpher”

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Wordnet• Type “wn word” on a machine where

wordnet is installed…• Large exception dictionary:• Demo

aardwolves aardwolf abaci abacus abacuses abacus abbacies abbacy abhenries abhenry abilities ability abkhaz abkhaz abnormalities abnormality aboideaus aboideau aboideaux aboideau aboiteaus aboiteau aboiteaux aboiteau abos abo abscissae abscissa abscissas abscissa absurdities absurdity…

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Using NLP• Strzalkowski (in Reader)

Text NLP repres Dbasesearch

TAGGERNLP: PARSER TERMS

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Using NLP

INPUT SENTENCEThe former Soviet President has been a local hero ever sincea Russian tank invaded Wisconsin.

TAGGED SENTENCEThe/dt former/jj Soviet/jj President/nn has/vbz been/vbn a/dt local/jj hero/nn ever/rb since/in a/dt Russian/jj tank/nn invaded/vbd Wisconsin/np ./per

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Using NLP

TAGGED & STEMMED SENTENCEthe/dt former/jj soviet/jj president/nn have/vbz be/vbn a/dt local/jj hero/nn ever/rb since/in a/dt russian/jj tank/nn invade/vbd wisconsin/np ./per

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Using NLP

PARSED SENTENCE[assert [[perf [have]][[verb[BE]] [subject [np[n PRESIDENT][t_pos THE] [adj[FORMER]][adj[SOVIET]]]] [adv EVER] [sub_ord[SINCE [[verb[INVADE]] [subject [np [n TANK][t_pos A] [adj [RUSSIAN]]]] [object [np [name [WISCONSIN]]]]]]]]]

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Using NLP

EXTRACTED TERMS & WEIGHTSPresident 2.623519 soviet 5.416102President+soviet 11.556747 president+former 14.594883Hero 7.896426 hero+local 14.314775Invade 8.435012 tank 6.848128Tank+invade 17.402237 tank+russian 16.030809Russian 7.383342 wisconsin 7.785689

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Same Sentence, different sysEnju ParserROOT ROOT ROOT ROOT -1 ROOT been be VBN VB 5been be VBN VB 5 ARG1 President president NNP NNP 3been be VBN VB 5 ARG2 hero hero NN NN 8a a DT DT 6 ARG1 hero hero NN NN 8a a DT DT 11 ARG1 tank tank NN NN 13local local JJ JJ 7 ARG1 hero hero NN NN 8The the DT DT 0 ARG1 President president NNP NNP 3former former JJ JJ 1 ARG1 President president NNP NNP 3Russian russian JJ JJ 12 ARG1 tank tank NN NN 13Soviet soviet NNP NNP 2 MOD President president NNP NNP 3invaded invade VBD VB 14 ARG1 tank tank NN NN 13invaded invade VBD VB 14 ARG2 Wisconsin wisconsin NNP NNP 15has have VBZ VB 4 ARG1 President president NNP NNP 3has have VBZ VB 4 ARG2 been be VBN VB 5since since IN IN 10 MOD been be VBN VB 5since since IN IN 10 ARG1 invaded invade VBD VB 14ever ever RB RB 9 ARG1 since since IN IN 10

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Other Considerations• Church (SIGIR 1995) looked at

correlations between forms of words in texts

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Assumptions in IR• Statistical independence of terms• Dependence approximations

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Statistical Independence Two events x and y are statistically

independent if the product of their probability of their happening individually equals their probability of happening together.

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Statistical Independence and Dependence• What are examples of things that are

statistically independent?

• What are examples of things that are statistically dependent?

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• How likely is a red car to drive by given we’ve seen a black one?

• How likely is the word “ambulence” to appear, given that we’ve seen “car accident”?

• Color of cars driving by are independent (although more frequent colors are more likely)

• Words in text are not independent (although again more frequent words are more likely)

Statistical Independence vs. Statistical Dependence

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Lexical Associations• Subjects write first word that comes to mind

– doctor/nurse; black/white (Palermo & Jenkins 64)• Text Corpora yield similar associations• One measure: Mutual Information (Church and Hanks

89)

• If word occurrences were independent, the numerator and denominator would be equal (if measured across a large collection)

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Interesting Associations with “Doctor”

(AP Corpus, N=15 million, Church & Hanks 89)

I(x,y) f(x,y) f(x) x f(y) y11.311.310.79.49.08.98.7

12830861125

1111105110511052751105621

honorarydoctorsdoctorsdoctorsexamineddoctorsdoctor

621442411546213171407

doctordentistsnursestreatingdoctortreatbills

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These associations were likely to happen because the non-doctor words shown here are very commonand therefore likely to co-occur with any noun.

Un-Interesting Associations with “Doctor”

I(x,y) f(x,y) f(x) x f(y) y0.960.950.93

64112

62128469084716

doctorais

7378511051105

withdoctorsdoctors

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Query Processing• Once the text is in a form to match to the

indexes then the fun begins– What approach to use?

• Boolean?• Extended Boolean?• Ranked

– Fuzzy sets?– Vector?– Probabilistic?– Language Models? – Neural nets?

• Most of the next few weeks will be looking at these different approaches

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Display and formatting• Have to present the the results to the user• Lots of different options here, mostly

governed by – How the actual document is stored – And whether the full document or just the

metadata about it is presented

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Review• Review



Model

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What to do with terms…• Once terms have been extracted from the

documents, they need to be stored in some way that lets you get back to documents that those terms came from

• The most common index structure to do this in IR systems is the “Inverted File”

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Boolean Implementation: Inverted Files

• We will look at “Vector files” in detail later. But conceptually, an Inverted File is a vector file “inverted” so that rows become columns and columns become rows

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How Are Inverted Files Created• Documents are parsed to extract

words (or stems) and these are saved with the Document ID.

Now is the timefor all good men

to come to the aidof their country

Doc 1

It was a dark andstormy night in

the country manor. The time was past midnight

Doc 2

TextProcSteps

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How Inverted Files are Created• After all document have

been parsed the inverted file is sorted

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How Inverted Files are Created• Multiple term

entries for a single document are merged and frequency information added

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Inverted Files• The file is commonly split into a Dictionary

and a Postings file

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Inverted Files• Lots of alternative implementations

– E.g.: Cheshire builds within-document frequency using a hash table during document parsing. Then Document IDs and frequency info are stored in a BerkeleyDB B-tree index keyed by the term.

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Btree (conceptual)

B | | D | | F |

AcesBoilers

Cars

F | | P | | Z |

R | | S | | Z |H | | L | | P |

DevilsMinors

PanthersSeminoles

FlyersHawkeyesHoosiers

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Btree with Postings

B | | D | | F |

AcesBoilers

Cars

F | | P | | Z |

R | | S | | Z |H | | L | | P |

DevilsMinors

PanthersSeminoles

FlyersHawkeyesHoosiers

2,4,8,122,4,8,122,4,8,12

2,4,8,122,4,8,12

2,4,8,125, 7, 200

2,4,8,122,4,8,128,120

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Inverted files• Permit fast search for individual terms• Search results for each term is a list of

document IDs (and optionally, frequency, part of speech and/or positional information)

• These lists can be used to solve Boolean queries:– country: d1, d2– manor: d2– country and manor: d2

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Review• Review



Model

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Now we have a system…• Except for the matching and ranking

between the query representation and the document representation– Stored in the inverted files

• We will start to take a look at one model for matching today

• The Boolean Model

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IR Models• Set Theoretic Models

– Boolean– Fuzzy– Extended Boolean

• Vector Models (Algebraic)• Probabilistic Models (probabilistic)• Others (e.g., neural networks, etc.)

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Boolean Model for IR• Based on Boolean Logic (Algebra of Sets).• Fundamental principles established by

George Boole in the 1850’s• Deals with set membership and operations

on sets• Set membership in IR systems is usually

based on whether (or not) a document contains a keyword (term)

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• Intersection – Boolean ‘AND’ -- -- • Union – Boolean ‘OR’ -- --• Negation – Boolean ‘NOT’ -- --

– Usually means “AND NOT” in IR • Exclusive OR – ‘XOR’ – seldom used,

– Instead

Boolean Operations on Sets

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Boolean Logic

A B

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Boolean Logic

A B

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Boolean Logic

A B

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Boolean Logic

A B

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Boolean Logic

A B

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Boolean Logic

A B

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Boolean Logic

A B

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Boolean Logic

A B

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Query Languages• A way to express the query (formal

expression of the information need)• Types:

– Boolean– Natural Language– Stylized Natural Language– Form-Based (GUI)

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Simple query language: Boolean• Terms + Boolean operators

– terms• words• normalized (stemmed) words• phrases• thesaurus terms

– Operators• AND• OR• NOT

– parentheses (for grouping operations)

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Boolean Queries• Cat• Cat OR Dog• Cat AND Dog• (Cat AND Dog)• (Cat AND Dog) OR Collar• (Cat AND Dog) OR (Collar AND Leash)• (Cat OR Dog) AND (Collar OR Leash)

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Boolean Queries• (Cat OR Dog) AND (Collar OR Leash)

– Each of the following combinations works:

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Boolean Queries

• (Cat OR Dog) AND (Collar OR Leash)– None of the following combinations works:

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Boolean Queries• Usually expressed as INFIX operators in IR

– ((a AND b) OR (c AND b))• NOT is UNARY PREFIX operator

– ((a AND b) OR (c AND (NOT b)))• AND and OR can be n-ary operators

– (a AND b AND c AND d)• Some rules - (De Morgan revisited)

– NOT(a) AND NOT(b) = NOT(a OR b)– NOT(a) OR NOT(b)= NOT(a AND b)– NOT(NOT(a)) = a

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Boolean Searching

Formal Query:cracks AND beamsAND Width_measurementAND Prestressed_concrete

Cracks

Beams Widthmeasurement

Prestressedconcrete

Relaxed Query:(C AND B AND P) OR(C AND B AND W) OR(C AND W AND P) OR(B AND W AND P)

Relaxed Query:(C AND B AND P) OR(C AND B AND W) OR(C AND W AND P) OR(B AND W AND P)

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Boolean Logic

t3

t1 t2

D1D2

D3

D4D5

D6

D8 D7

D9

D10

D11

m1m2

m3m5

m4

m7m8

m6

m2 = t1 t2 t3

m1 = t1 t2 t3

m4 = t1 t2 t3

m3 = t1 t2 t3

m6 = t1 t2 t3

m5 = t1 t2 t3

m8 = t1 t2 t3

m7 = t1 t2 t3

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Precedence Ordering• In what order do we evaluate the

components of the Boolean expression?– Parenthesis get done first

• (a or b) and (c or d)• (a or (b and c) or d)

– Usually start from the left and work right (in case of ties)

– Usually (if there are no parentheses)• NOT before AND• AND before OR

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Next Time• More on the Boolean Model including

facetted searching, query parse trees and extended Boolean approaches

Lecture 4: Boolean IR System Elements

Documents

Transcript of Lecture 4: Boolean IR System Elements