Introducing Information Retrieval

and Web Search

Introduction to

Information Retrieval

Introduction to Information Retrieval

Information Retrieval

• Information Retrieval (IR) is finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need from within large collections (usually stored on computers).

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• These days we frequently think first of web search, but there are many other cases:
• E-mail search
• Searching your laptop
• Corporate knowledge bases
• Legal information retrieval

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Introduction to Information Retrieval

Unstructured (text) vs. structured (database) data in the mid-nineties

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Introduction to Information Retrieval

Unstructured (text) vs. structured (database) data today

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Introduction to Information Retrieval

Basic assumptions of Information Retrieval

• Collection: A set of documents
• Assume it is a static collection for the moment

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• Goal: Retrieve documents with information that is relevant to the user’s information need and helps the user complete a task

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Sec. 1.1

Introduction to Information Retrieval

The classic search model

how trap mice alive

Collection

User task

Info need�

Query�

Results�

Search

engine�

Query�refinement

Get rid of mice in a politically correct way

Info about removing mice

without killing them

Search

Introduction to Information Retrieval

How good are the retrieved docs?

• Precision : Fraction of retrieved docs that are relevant to the user’s information need
• Recall : Fraction of relevant docs in collection that are retrieved

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• More precise definitions and measurements to follow later

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Sec. 1.1

Introduction to Information Retrieval

Introducing Information Retrieval

and Web Search

Introduction to

Information Retrieval

Introduction to Information Retrieval

Term-document incidence matrices

Introduction to

Information Retrieval

Introduction to Information Retrieval

Unstructured data in 1620

• 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?
• Why is that not the answer?
• Slow (for large corpora)
• NOT Calpurnia is non-trivial
• Other operations (e.g., find the word Romans near countrymen) not feasible
• Ranked retrieval (best documents to return)
• Later lectures

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Sec. 1.1

Introduction to Information Retrieval

Term-document incidence matrices

1 if play contains word, 0 otherwise

Brutus AND Caesar BUT NOT Calpurnia

Sec. 1.1

Introduction to Information Retrieval

Incidence vectors

• So we have a 0/1 vector for each term.
• To answer query: take the vectors for Brutus, Caesar and Calpurnia (complemented) 🡺 bitwise AND.
• 110100 AND
• 110111 AND
• 101111 =
• 100100

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Sec. 1.1

Introduction to Information Retrieval

Answers to query

• Antony and Cleopatra, Act III, Scene ii

Agrippa [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.

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• Hamlet, Act III, Scene ii

Lord Polonius: I did enact Julius Caesar I was killed i’ the

Capitol; Brutus killed me.

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Sec. 1.1

Introduction to Information Retrieval

Bigger collections

• Consider N = 1 million documents, each with about 1000 words.
• Avg 6 bytes/word including spaces/punctuation
• 6GB of data in the documents.
• Say there are M = 500K distinct terms among these.

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Sec. 1.1

Introduction to Information Retrieval

Can’t build the matrix

• 500K x 1M matrix has half-a-trillion 0’s and 1’s.

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• But it has no more than one billion 1’s.
• matrix is extremely sparse.

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• What’s a better representation?
• We only record the 1 positions.

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Why?

Sec. 1.1

Introduction to Information Retrieval

Term-document incidence matrices

Introduction to

Information Retrieval

Introduction to Information Retrieval

The Inverted Index

The key data structure underlying modern IR

Introduction to

Information Retrieval

Introduction to Information Retrieval

Inverted index

• For each term t, we must store a list of all documents that contain t.
• Identify each doc by a docID, a document serial number
• Can we used fixed-size arrays for this?

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

Sec. 1.2

Introduction to Information Retrieval

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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Sorted by docID (more later on why).

Sec. 1.2

Brutus

Calpurnia

Caesar

2

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174

54

101

Introduction to Information Retrieval

Inverted index construction

Documents to

be indexed

Friends, Romans, countrymen.

Sec. 1.2

Introduction to Information Retrieval

Inverted index construction

Documents to

be indexed

Friends, Romans, countrymen.

Sec. 1.2

Introduction to Information Retrieval

Initial stages of text processing

• Tokenization
• Cut character sequence into word tokens
• Deal with “John’s”, a state-of-the-art solution
• Normalization
• Map text and query term to same form
• You want U.S.A. and USA to match
• Stemming
• We may wish different forms of a root to match
• authorize, authorization
• Stop words
• We may omit very common words (or not)
• the, a, to, of

Introduction to Information Retrieval

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

Sec. 1.2

Introduction to Information Retrieval

Indexer steps: Sort

• Sort by terms
• And then docID

Core indexing step

Sec. 1.2

Introduction to Information Retrieval

Indexer steps: Dictionary & Postings

• Multiple term entries in a single document are merged.
• Split into Dictionary and Postings
• Doc. frequency information is added.

Why frequency?

Will discuss later.

Sec. 1.2

Introduction to Information Retrieval

Where do we pay in storage?

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Pointers

Terms and counts

IR system implementation

• How do we index efficiently?
• How much storage do we need?

Sec. 1.2

Lists of docIDs

Introduction to Information Retrieval

The Inverted Index

The key data structure underlying modern IR

Introduction to

Information Retrieval

Introduction to Information Retrieval

Query processing with an inverted index

Introduction to

Information Retrieval

Introduction to Information Retrieval

The index we just built

• How do we process a query?
• Later - what kinds of queries can we process?

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Our focus

Sec. 1.3

Introduction to Information Retrieval

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” the two postings (intersect the document sets):

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128

34

Brutus

Caesar

Sec. 1.3

Introduction to Information Retrieval

The merge

• Walk through the two postings simultaneously, in time linear in the total number of postings entries

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

operations.

Crucial: postings sorted by docID.

Sec. 1.3

Introduction to Information Retrieval

The merge

• Walk through the two postings simultaneously, in time linear in the total number of postings entries

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128

34

2

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

operations.

Crucial: postings sorted by docID.

Sec. 1.3

Introduction to Information Retrieval

Intersecting two postings lists�(a “merge” algorithm)

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Introduction to Information Retrieval

Query processing with an inverted index

Introduction to

Information Retrieval

Introduction to Information Retrieval

The Boolean Retrieval Model

& Extended Boolean Models

Introduction to

Information Retrieval

Introduction to Information Retrieval

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.
• Perhaps the simplest model to build an IR system on
• Primary commercial retrieval tool for 3 decades.
• Many search systems you still use are Boolean:
• Email, library catalog, Mac OS X Spotlight

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Sec. 1.3

Introduction to Information Retrieval

Example: WestLaw http://www.westlaw.com/

• Largest commercial (paying subscribers) legal search service (started 1975; ranking added 1992; new federated search added 2010)
• Tens of terabytes of data; ~700,000 users
• Majority of users still use boolean queries
• Example query:
• What is the statute of limitations in cases involving the federal tort claims act?
• LIMIT! /3 STATUTE ACTION /S FEDERAL /2 TORT /3 CLAIM
• /3 = within 3 words, /S = in same sentence

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Sec. 1.4

Introduction to Information Retrieval

Example: WestLaw http://www.westlaw.com/

• Another example query:
• Requirements for disabled people to be able to access a workplace
• disabl! /p access! /s work-site work-place (employment /3 place
• Note that SPACE is disjunction, not conjunction!
• Long, precise queries; proximity operators; incrementally developed; not like web search
• Many professional searchers still like Boolean search
• You know exactly what you are getting
• But that doesn’t mean it actually works better….

Sec. 1.4

Introduction to Information Retrieval

Boolean queries: �More general merges

• Exercise: Adapt the merge for the queries:

Brutus AND NOT Caesar

Brutus OR NOT Caesar

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• Can we still run through the merge in time O(x+y)? What can we achieve?

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Sec. 1.3

Introduction to Information Retrieval

Merging

What about an arbitrary Boolean formula?

(Brutus OR Caesar) AND NOT

(Antony OR Cleopatra)

• Can we always merge in “linear” time?
• Linear in what?
• Can we do better?

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Sec. 1.3

Introduction to Information Retrieval

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

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Query: Brutus AND Calpurnia AND Caesar

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Sec. 1.3

Introduction to Information Retrieval

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

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Introduction to Information Retrieval

More general optimization

• e.g., (madding OR crowd) AND (ignoble OR strife)
• Get doc. freq.’s 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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Sec. 1.3

Introduction to Information Retrieval

Exercise

• Recommend a query processing order for

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• Which two terms should we process first?

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(tangerine OR trees) AND

(marmalade OR skies) AND

(kaleidoscope OR eyes)

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Introduction to Information Retrieval

Query processing exercises

• Exercise: If the query is friends AND romans AND (NOT countrymen), how could we use the freq of countrymen?
• Exercise: Extend the merge to an arbitrary Boolean query. Can we always guarantee execution in time linear in the total postings size?
• Hint: Begin with the case of a Boolean formula query: in this, each query term appears only once in the query.

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Introduction to Information Retrieval

Exercise

• Try the search feature at http://www.rhymezone.com/shakespeare/
• Write down five search features you think it could do better

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Introduction to Information Retrieval

The Boolean Retrieval Model

& Extended Boolean Models

Introduction to

Information Retrieval

Introduction to Information Retrieval

Phrase queries and positional indexes

Introduction to

Information Retrieval

Introduction to Information Retrieval

Phrase queries

• We want to be able to answer queries such as “stanford university” – as a phrase
• Thus the sentence “I went to university at Stanford” is not a match.
• The concept of phrase queries has proven easily understood by users; one of the few “advanced search” ideas that works
• Many more queries are implicit phrase queries
• For this, it no longer suffices to store only

<term : docs> entries

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Sec. 2.4

Introduction to Information Retrieval

A first attempt: Biword indexes

• Index every consecutive pair of terms in the text as a phrase
• For example the text “Friends, Romans, Countrymen” would generate the biwords
• friends romans
• romans countrymen
• Each of these biwords is now a dictionary term
• Two-word phrase query-processing is now immediate.

Sec. 2.4.1

Introduction to Information Retrieval

Longer phrase queries

• Longer phrases can be processed by breaking them down
• stanford university palo alto can be broken into the Boolean query on biwords:

stanford university AND university palo AND palo alto

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Without the docs, we cannot verify that the docs matching the above Boolean query do contain the phrase.

Can have false positives!

Sec. 2.4.1

Introduction to Information Retrieval

Extended biwords

• Parse the indexed text and perform part-of-speech-tagging (POST).
• Bucket the terms into (say) Nouns (N) and articles/prepositions (X).
• Call any string of terms of the form NX*N an extended biword.
• Each such extended biword is now made a term in the dictionary.
• Example: catcher in the rye

N X X N

• Query processing: parse it into N’s and X’s
• Segment query into enhanced biwords
• Look up in index: catcher rye

Sec. 2.4.1

Introduction to Information Retrieval

Issues for biword indexes

• False positives, as noted before
• Index blowup due to bigger dictionary
• Infeasible for more than biwords, big even for them

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• Biword indexes are not the standard solution (for all biwords) but can be part of a compound strategy

Sec. 2.4.1

Introduction to Information Retrieval

Solution 2: Positional indexes

• In the postings, store, for each term the position(s) in which tokens of it appear:

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<term, number of docs containing term;

doc1: position1, position2 … ;

doc2: position1, position2 … ;

etc.>

Sec. 2.4.2

Introduction to Information Retrieval

Positional index example

• For phrase queries, we use a merge algorithm recursively at the document level
• But we now need to deal with more than just equality

<be: 993427;

1: 7, 18, 33, 72, 86, 231;

2: 3, 149;

4: 17, 191, 291, 430, 434;

5: 363, 367, …>

Which of docs 1,2,4,5

could contain “to be

or not to be”?

Sec. 2.4.2

Introduction to Information Retrieval

Processing a phrase query

• Extract inverted index entries for each distinct term: to, be, or, not.
• Merge their doc:position lists to enumerate all positions with “to be or not to be”.
• to:
• 2:1,17,74,222,551; 4:8,16,190,429,433; 7:13,23,191; ...
• be:
• 1:17,19; 4:17,191,291,430,434; 5:14,19,101; ...
• Same general method for proximity searches

Sec. 2.4.2

Introduction to Information Retrieval

Proximity queries

• LIMIT! /3 STATUTE /3 FEDERAL /2 TORT
• Again, here, /k means “within k words of”.
• Clearly, positional indexes can be used for such queries; biword indexes cannot.
• Exercise: Adapt the linear merge of postings to handle proximity queries. Can you make it work for any value of k?
• This is a little tricky to do correctly and efficiently
• See Figure 2.12 of IIR

Sec. 2.4.2

Introduction to Information Retrieval

Positional index size

• A positional index expands postings storage substantially
• Even though indices can be compressed
• Nevertheless, a positional index is now standardly used because of the power and usefulness of phrase and proximity queries … whether used explicitly or implicitly in a ranking retrieval system.

Sec. 2.4.2

Introduction to Information Retrieval

Positional index size

• Need an entry for each occurrence, not just once per document
• Index size depends on average document size
• Average web page has <1000 terms
• SEC filings, books, even some epic poems … easily 100,000 terms
• Consider a term with frequency 0.1%

Why?

Sec. 2.4.2

Introduction to Information Retrieval

Rules of thumb

• A positional index is 2–4 as large as a non-positional index

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• Positional index size 35–50% of volume of original text

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• Caveat: all of this holds for “English-like” languages

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Sec. 2.4.2

Introduction to Information Retrieval

Combination schemes

• These two approaches can be profitably combined
• For particular phrases (“Michael Jackson”, “Britney Spears”) it is inefficient to keep on merging positional postings lists
• Even more so for phrases like “The Who”
• Williams et al. (2004) evaluate a more sophisticated mixed indexing scheme
• A typical web query mixture was executed in ¼ of the time of using just a positional index
• It required 26% more space than having a positional index alone

Sec. 2.4.3

Introduction to Information Retrieval

Phrase queries and positional indexes

Introduction to

Information Retrieval

Introduction to Information Retrieval