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L01: Corpuses, Terms and Search

Basic terminology

The need for unstructured text search

Boolean Retrieval Model

Algorithms for compressing data

Algorithms for answering Boolean queries

Read Chapter 1 of MRS

Unstructured (text) vs. structured (database) data

31996

Unstructured (text) vs. structured (database) data

42006

Text Information Retrieval

You have all the Shakespeare plays stored in files

Which plays contain the word Caesar? Your algorithm here:

Which plays of Shakespeare contain the words Brutus AND Caesar ?

Processing unstructured data

Which plays of Shakespeare contain the words Brutus AND Caesar but NOT Calpurnia?

One could grep all of Shakespeare’s plays for Brutus and Caesar, then strip out lines containing Calpurnia, but…

It Is Slow! (for large corpora)Computing NOT Calpurnia is non-trivial

Other operations not feasible (e.g., find Romans near countrymen)

Ranked retrieval (find “best” documents to return)also not possible

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Term-document incidence

Antony and Cleopatra Julius Caesar The Tempest Hamlet Othello Macbeth

Antony 1 1 0 0 0 1

Brutus 1 1 0 1 0 0

Caesar 1 1 0 1 1 1

Calpurnia 0 1 0 0 0 0

Cleopatra 1 0 0 0 0 0

mercy 1 0 1 1 1 1

worser 1 0 1 1 1 0

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1 if file contains word, 0 otherwise

Brutus AND Caesar but NOT Calpurnia

N documents

m t

erm

s

Incidence vectors

So we have a 0/1 binary vector for each term.

To answer query: take the vectors for Brutus, Caesar and (complement) Calpurnia bitwise AND.

110100 AND 110111 AND 101111 100100.

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N documents

m t

erm

s

Brutus AND Caesar but NOT Calpurnia

Answers to query

Antony and Cleopatra, Act III, Scene iiAgrippa [Aside to DOMITIUS ENOBARBUS]: Why, Enobarbus,

When Antony found Julius Caesar dead,

He cried almost to roaring; and he wept

When at Philippi he found Brutus slain.

Hamlet, Act III, Scene iiLord Polonius: I did enact Julius Caesar I was killed i' the

Capitol; Brutus killed me.

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Space requirements

Say, N = 1M documents, each with about 1K terms. How many terms all together?

Average 6 bytes/term including spaces/punctuation.How many GB of data?

Say there are m = 500K distinct terms among these.How many 0’s and 1’s in the Term-doc incidence matrix?

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N=1mil documents

M=

0.5

mil t

erm

s

Does the matrix fits in memory?

500K x 1M matrix has 500 Billion 0’s and 1’s.

But matrix is extremely sparse it has no more than 1 Billion 1’s.

What’s a better representation than a matrix?

We only record the 1 positions.11

Why?

N documents

m t

erm

s 1 1 1

1 1

1

1 1

1 1

1 1

1 1

1 1

1 1

1 1

1 1

1 1

1 1

1

1 1

1 1

1 1

1 11 1

1

1 1

1

1 1

1 1

“Inverted” Index

For each term T, we must store a listing of all document id’s that contain T.

Do we use an array or a linked list for this?

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Brutus

Calpurnia

Caesar

1 2 3 5 8 13 21 34

2 4 8 16 32 64128

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What happens if the word Caesar is added to document 14?

Inverted index

Linked lists generally preferred to arrays + Dynamic space allocation + Insertion of terms into documents easy – Space overhead of pointers

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Brutus

Calpurnia

Caesar

2 4 8 16 32 64 128

2 3 5 8 13 21 34

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1

Dictionary Postings lists

Sorted by docID

Posting

Inverted index construction

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Tokenizer

Token stream. Friends Romans countrymen

Linguistic modules

Modified tokens. friend roman countryman

Indexer

Inverted index.

friend

roman

countryman

2 4

2

13 16

1

More onthese later.

Documents tobe indexed.

Friends, Romans, countrymen.

Indexer step 1: Token sequence

INPUT: Sequence of pairs:(Modified token, Document ID)

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I did enact JuliusCaesar I was killed

i' the Capitol; Brutus killed me.

Doc 1

So let it be withCaesar. The noble

Brutus hath told youCaesar was ambitious

Doc 2

Indexer step 2: Sort

Sort by terms.

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Core indexing step.

Indexer step 3: Dictionary & Postings

Merge multiple term entries in a single document

Note two entries for caesar! We merge PER DOCUMENT.

Add frequency information.

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Why frequency?Will discuss later.

Dictionary

Postings

Query processing: AND

Consider processing the query:Brutus AND Caesar Locate Brutus in the Dictionary;

Retrieve its postings. Locate Caesar in the Dictionary;

Retrieve its postings. “Intersect” the two postings:

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128

34

2 4 8 16 32 64

1 2 3 5 8 13

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Brutus

Caesar

The Intersection

Walk through the two postings simultaneously.

What did we call this process in CS230?

Time? _______ in the total number of postings entries

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34

1282 4 8 16 32 64

1 2 3 5 8 13 21

128

34

2 4 8 16 32 64

1 2 3 5 8 13 21

Brutus

Caesar2 8

If the list lengths are x and y, the merge takes O(x+y)

operations.

Crucial: postings sorted by docID.

Intersecting two postings lists

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Query Optimization

Best order for processing pairs of postings? (Brutus AND Calpurnia) AND Caesar (Calpurnia AND Caesar) AND Brutus (Brutus AND Caesar) AND Calpurnia

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Query: Brutus AND Calpurnia AND Caesar

Brutus

Calpurnia

Caesar

1 2 3 5 8 13 21 34

2 4 8 16 32 64128

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Query optimization example

Process in order of increasing freq: start with smallest set, then keep cutting further.

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Brutus

Calpurnia

Caesar

1 2 3 5 8 13 21 34

2 4 8 16 32 64128

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This is why we keptfreq in dictionary

Execute the query as (Caesar AND Brutus) AND Calpurnia.

Query: Brutus AND Calpurnia AND Caesar

More General Optimization

Query: (madding OR crowd) AND (ignoble OR strife)

Get freq’s for all terms.

Estimate the size of each OR by the sum of its freq’s (conservative).

Process in increasing order of OR sizes.

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Boolean Queries: Exact match

The Boolean Retrieval model is being able to ask a query that is a Boolean expression:

Boolean Queries are queries using AND, OR and NOT to join query terms

Views each document as a set of words Is precise: document matches condition or not.

Primary commercial retrieval tool for 3 decades.

Professional searchers (e.g., lawyers) still like Boolean queries: (e.g. Westlaw)

You know exactly what you’re getting.

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