Boolean retrieval & basics of...
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Boolean retrieval & basics of indexingCE-324: Modern Information Retrieval Sharif University of Technology
M. Soleymani
Fall 2016
Most slides have been adapted from: Profs. Manning, Nayak & Raghavan lectures (CS-276, Stanford)
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Boolean retrieval model
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Query: Boolean expressions
Boolean queries use AND, OR and NOT to join query terms
Views each doc as a set of words
Term-incidence matrix is sufficient
Shows presence or absence of terms in each doc
Perhaps the simplest model to build an IR system on
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Boolean queries: Exact match
In pure Boolean model, retrieved docs are not ranked
Result is a set of docs.
It is precise or exact match (docs match condition or not).
Primary commercial retrieval tool for 3 decades (Until
1990’s).
Many search systems you still use are Boolean:
Email, library catalog, Mac OS X Spotlight
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The classic search model
Task
Info Need
Query
Verbal form
Results
SEARCH
ENGINE
Query
Refinement
Get rid of mice in a
politically correct way
Info about removing mice
without killing them
How do I trap mice alive?
mouse trap
Misconception?
Mistranslation?
Misformulation?
Corpus
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Example: Plays of Shakespeare
Which plays of Shakespeare contain the words Brutus
AND Caesar but NOT Calpurnia?
scanning all of Shakespeare’s plays for Brutus and Caesar, then
strip out those containing Calpurnia?
The above solution cannot be the answer for large
corpora (computationally expensive)
Efficiency is also an important issue (along with the
effectiveness)
Index: data structure built on the text to speed up the searches
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Example: Plays of Shakespeare
Term-document incidence matrix
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
1 if play contains
word, 0 otherwise
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Incidence vectors
So we have a 0/1 vector for each term.
Brutus AND Caesar but NOT Calpurnia
To answer query: take the vectors for Brutus, Caesar
and Calpurnia (complemented) bitwise AND.
110100 AND 110111 AND 101111 = 100100.
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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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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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Brutus AND Caesar but NOT Calpurnia
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Bigger collections
Number of docs: N = 106
Average length of a doc≈ 1000 words
No. of distinct terms: M = 500,000
Average length of a word ≈ 6 bytes
including spaces/punctuation
6GB of data
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Sparsity of Term-document incidence matrix
500K x 1M matrix has half-a-trillion 0’s and 1’s.
But it has no more than one billion 1’s.
matrix is extremely sparse.
so a minimum of 99.8% of the cells are zero.
What’s a better representation?
We only record the 1 positions.
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Why?
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Inverted index
For each term t, store a list of all docs that contain t.
Identify each by a docID, a document serial number
Can we use fixed-size arrays for this?
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Brutus
Calpurnia
Caesar 1 2 4 5 6 16 57 132
1 2 4 11 31 45173
2 31
What happens if the word Caesar
is added to doc 14?
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54101
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Inverted index
We need variable-size postings lists
On disk, a continuous run of postings is normal and best
In memory, can use linked lists or variable length arrays
Some tradeoffs in size/ease of insertion
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Dictionary Postings
Sorted by docID
Posting
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Brutus
Calpurnia
Caesar 1 2 4 5 6 16 57 132
1 2 4 11 31 45173
2 31
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54101
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Tokenizer
Token stream Friends Romans Countrymen
Inverted index construction
Linguistic modules
Modified tokens
friend roman countryman
Indexer
Inverted index
friend
roman
countryman
2 4
2
13 16
1
We will see
more on
these later.
Docs to
be indexed
Friends, Romans, countrymen.
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Indexer steps: Token sequence
Sequence of (Modified token, Document ID) pairs.
I did enact Julius
Caesar I was killed
i' the Capitol;
Brutus killed me.
Doc 1
So let it be with
Caesar. The noble
Brutus hath told you
Caesar was ambitious
Doc 2
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Indexer steps: Sort
Sort by terms
And then docID
Core indexing step
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Indexer steps: Dictionary & Postings
Multiple term entries in a single doc are merged.
Split into Dictionary and Postings
Document frequency information is added.
Why frequency?
Will discuss later.
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Where do we pay in storage?
17Pointers
Terms and
counts
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Lists of
docIDs
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A naïve dictionary
An array of struct:
char[20] int Postings *
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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.
“Merge” (intersect) the two postings:
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128
34
2 4 8 16 32 64
1 2 3 5 8 13 21
Brutus
Caesar
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The merge
Walk through the two postings simultaneously, in time
linear in the total number of postings entries
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If list lengths are x and y, merge takes O(x+y) operations.
Crucial: postings sorted by docID.
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128
31
2 4 8 41 48 64
1 2 3 8 11 17 21
Brutus
Caesar
2 8
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Intersecting two postings lists
(a “merge” algorithm)
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Boolean queries: More general merges
Exercise:Adapt the merge for the queries:
Brutus AND NOT Caesar
Brutus OR NOT Caesar
Can we still run through the merge in time 𝑂(𝑥 + 𝑦)?
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Merging
What about an arbitrary Boolean formula?
(Brutus OR Caesar) AND NOT (Antony OR Cleopatra)
Can we merge in “linear” time for general Boolean
queries?
Linear in what?
Can we do better?
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Query optimization
What is the best order for query processing?
Consider a query that is an AND of 𝑛 terms.
For each of the 𝑛 terms, get its postings, then AND
them together.
Brutus
Caesar
Calpurnia
1 2 3 5 8 16 21 34
2 4 8 16 32 64128
13 16
Query: Brutus AND Calpurnia AND Caesar
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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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This is why we kept
document freq. in dictionary
Execute the query as (Calpurnia AND Brutus) AND Caesar.
Sec. 1.3
Brutus
Caesar
Calpurnia
1 2 3 5 8 16 21 34
2 4 8 16 32 64128
13 16
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More general optimization
Example:
(madding OR crowd) AND (ignoble OR strife)
Get doc frequencies for all terms.
Estimate the size of each OR by the sum of its
doc. freq.’s (conservative).
Process in increasing order of OR sizes.
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Faster postings merges: skip lists
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Augment postings with skip pointers
It is useful for AND queries
To skip postings that will not figure in the results.
Where do we place skip pointers?
1282 4 8 41 48 64
311 2 3 8 11 17 21
3111
41 128
Sec. 2.3
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Query processing with skip pointers
1282 4 8 41 48 64
311 2 3 8 11 17 21
3111
41 128
Suppose we are processing 8 on each list. We
match it and advance.
We then have 41 and 11.
The skip successor of 11 is 31 (31<41). So, we
can skip ahead past the intervening postings.
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Where do we place skips?
Tradeoff:
More skips shorter skip spans
More likely to skip but lots of comparisons to skip pointers (and also
more space for them)
Fewer skips long skip spans
few successful skips but also few pointer comparison (and also less
space for them)
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Placing skips
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Simple heuristic
For posting of length 𝐿, use 𝐿 evenly-spaced skip pointers
Easy if the index is relatively static
This ignores the distribution of query terms
This definitely used to help; with modern hardware it may
not unless you’re memory-based (Bahle et. al 2002)
The I/O cost of loading bigger postings list can outweigh the
gains from in memory merging
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Summary of Boolean IR
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Advantages of exact match
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It can be implemented very efficiently
Predictable, easy to explain
precise semantics
Structured queries for pinpointing precise docs
neat formalism
Work well when you know exactly (or roughly) what the
collection contains and what you’re looking for
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Disadvantages of the Boolean Model
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Query formulation (Boolean expression) is difficult for
most users
Too simplistic Boolean queries by most users
AND, OR as opposite extremes in a precision/recall tradeoff
Usually either too few or too many docs in response to a user query
Retrieval based on binary decision criteria
No ranking of the docs is provided
Difficulty increases with collection size
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Ranking results in advanced IR models
Boolean queries give inclusion or exclusion of docs.
Results of queries in Boolean model as a set
Modern information retrieval systems are no longer
based on the Boolean model
Often we want to rank/group results
Need to measure proximity from query to each doc.
Index term weighting can provide a substantial improvement
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