Transcript Introduction to Information Retrieval Acknowledgements to Pandu Nayak and Prabhakar Raghavan of Stanford, Hinrich Schütze and Christina Lioma of Stutgart, Lee Giles (and his sources)

Introduction to Information
Retrieval
Acknowledgements to Pandu Nayak and
Prabhakar Raghavan of Stanford, Hinrich
Schütze and Christina Lioma of Stutgart, Lee
Giles (and his sources) of Penn State
So far
• You have learned to crawl the web
– Being a good citizen and not hurting the
servers
– Extracting the kind of information that you
want
– Storing the retrieved material locally
• Now, what are you going to do with
those materials?
– Develop a way to retrieve what you want
from that collection, as you need it.
Finding what you need in a
collection of documents
• Information Retrieval:
– Given a collection of “documents”
– Retrieve
• One or more documents that contain the
specific information you want
• Obtain the answer to an information need by
querying the document collection
Tonight’s class
• Introduce the concepts of Information
Retrieval
– Assume you have a collection
– How to look into the documents for your
information need
– How to do it efficiently
Later
• Tools to build indices
– Apache Lucene
– Apache Solr
A brief introduction to
Information Retrieval
• Recall our primary resource:
– Christopher D. Manning, Prabhakar Raghavan and
Hinrich Schütze, Introduction to Information Retrieval,
Cambridge University Press. 2008.
• The entire book is available online, free, at
http://nlp.stanford.edu/IR-book/informationretrieval-book.html
• I will use some of the slides that they provide to go
with the book.
• I will also use slides from other sources and make
new ones. The primary other source is Dr. Lee
Giles, Penn State. Information Retrieval IST441
Author’s definition
• 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).
• Note the use of the word “usually.” We will
see examples where the material is not
documents, and not text.
Examples and Scaling
• IR is about finding a needle in a haystack
– finding some particular thing in a very
large collection of similar things.
• Our examples are necessarily small, so
that we can comprehend them. Do
remember, that all that we say must
scale to very large quantities.
Searching Shakespeare
• Which plays of Shakespeare contain the words
Brutus AND Caesar but NOT Calpurnia?
– See http://www.rhymezone.com/shakespeare/
• 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)
Document match
• Go to http://www.rhymezone.com/shakespeare/
• Enter, one at a time,
– Caesar
– Brutus
– Calpurnia
• For each, note the collection of plays that
contain the term
• We need to associate one or more plays
with each term – Term Incidence
For our example, we will use a subset of the plays
Term-document incidence
Brutus AND Caesar BUT NOT
Calpurnia
First approach – make a matrix with
terms on one axis and plays on the other
All the plays  

All the terms 

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
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.
Reminder: 0 AND 0 = 0, 0 AND 1 =0, 1 AND 0 = 0, 1 AND 1 = 1
The effect of the AND operation is 1 if the items are both 1
and 0 if they are not both 1
Answer 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.
• Hamlet, Act III, Scene ii
Lord Polonius: I did enact Julius Caesar I
was killed i' the Capitol;
Brutus killed me.
Spot check
• Try another one
• What is the vector for the query
– Antony and mercy
• What would we do to find Antony OR
mercy?
Basic assumptions about
information retrieval
• Collection: Fixed set of documents
• Goal: Retrieve documents with information
that is relevant to the user’s information
need and helps the user complete a task
The classic search model
TASK
Ultimately, some task to perform.
Info
Need
Some information is required in order
to perform the task.
Verbal
form
The information need must be
expressed in words (usually).
Query
The information need must be expressed in the
form of a query that can be processed.
It may be necessary
to rephrase the
query and try again
Query
Refinement
SEARCH
ENGINE
Results
Corpus
The classic search model
Potential pitfalls between
task and query results
Get rid of mice in a
politically correct way
TASK
Misconception?
Info about removing mice
without killing them
Info
Need
Mistranslation?
How do I trap mice alive?
Verbal
form
Misformulation?
mouse trap
Query
SEARCH
ENGINE
Query
Refinement
Results
Corpus
How good are the results?
• Precision: How well do the results
match the information need?
• Recall: What fraction of the available
correct results were retrieved?
• These are the basic concepts of
information retrieval evaluation.
Size considerations
• 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.
The matrix does not work
• 500K x 1M matrix has half-atrillion 0’s and 1’s.
• But it has no more than one billion
1’s.
– matrix is extremely sparse.
• What’s a better representation?
– We only record the 1 positions.
– i.e. We don’t need to know which
documents do not have a term, only
those that do.
Why?
Inverted index
• For each term t, we must store a list of all
documents that contain t.
– Identify each by a docID, a document serial
number
• Can we used fixed-size arrays for this?
Brutus
1
2
4
11
31
45 173 174
Caesar
1
2
4
5
6
16
Calpurnia
2
31 54
57 132
101
What happens if the word Caesar is added to document 14?
More likely, add document 14, which contains “Caesar.”
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
Brutus
1
Caesar
1
Calpurnia
Dictionary
2
2
2
31
Posting
4
11
31
45 173
4
5
6
16
174
57 132
54 101
Postings
Sorted by docID (more later on why).
22
Sec. 1.2
Inverted index construction
Documents to
be indexed.
Token stream.
Modified
tokens.
Friends, Romans, countrymen.
Tokenizer
Romans
Countrymen
friend
roman
countryman
Linguistic modules
Stop words, stemming,
capitalization, cases, etc.
Inverted index.
Friends
Indexer
friend
2
4
roman
1
2
countryman
13
16
Indexer steps: Token
sequence
 Sequence of (Modified token, Document
ID) pairs.
Initially, all the tokens from document 1, then
all the tokens from document 2, etc., without
regard for duplication.
Doc 1
I did enact Julius
Caesar I was killed
i' the Capitol;
Brutus killed me.
Doc 2
So let it be with
Caesar. The noble
Brutus hath told you
Caesar was ambitious
Indexer steps: Sort
 Sort by terms
 docID within terms
Core indexing step
Indexer steps: Dictionary & Postings
 Multiple
term entries
in a single
document
are merged.
 Split into
Dictionary
and Postings
 Doc.
frequency
information
is added.
Number of documents in which the term appears
Where do we pay in storage?
Lists of
docIDs
Terms
and
counts
Pointers
27
Storage
• A small diversion
• Computer storage
– Processor caches
– Main memory
– External storage (hard disk, other devices)
• Very substantial differences in access speeds
– Processor caches mostly used by the operating
system for rapid access to data that will be needed
soon
– Main memory.
• Limited quantities. High speed access
– Hard disk
• Much larger quantities, speed restricted, access in fixed
units (blocks)
Some size examples
• From the iMac
– Memory
• 4GB (two 2GB SO-DIMMs) of 1333MHz DDR3
SDRAM; four SO-DIMM slots support up to
16GB
– Hard drive
• 500GB or 1TB 7200-rpm Serial ATA hard drive
• Optional 2TB 7200-rpm Serial ATA hard drive
These are big numbers, but the potential size of a significant collection is
larger still. The steps taken to optimize use of storage are critical to
satisfactory response time.
Implications of size limits
virtual memory (on disk)
page
1
real memory (RAM)
slot
1
slot
2
slot
3
slot
4
slot
5
slot
6
slot
7
slot
8
slot
9
slot
10
slot
11
slot
12
page
2
page
3
page
4
... ... ... ... ...
... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ...
virtual page 2
generates a “page
fault” when
referencing virtual
page 71
virtual page 71 is brought
from disk into real memory
b
reference to page 71
... ... ... ... ... ... ... ... ...
... ... ... ... ... ...
page
70
page
71
page
72
How do we process a query?
• Using the index we just built, examine
the terms in some order, looking for the
terms in the query.
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:
2
4
8
16
1
2
3
5
32
8
64
13
128
21
34
Brutus
Caesar
32
Sec. 1.3
The merge
• Walk through the two postings
simultaneously, in time linear in the total
number of postings entries
2
8
2
4
8
16
1
2
3
5
32
8
64
13
128
21
34
Brutus
Caesar
If the list lengths are x and y, the merge takes O(x+y) operations.
What does that mean?
Crucial: postings sorted by docID.
33
Intersecting two postings lists
(a “merge” algorithm)
34
Spot Check
• Let’s assume that
– the term mercy appears in documents 1, 2,
13, 18, 24,35, 54
– the term noble appears in documents 1, 5,
7, 13, 22, 24, 56
• Show the document lists, then step
through the merge algorithm to obtain
the search results.
Sec. 1.3
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
• Primary commercial retrieval tool for 3
decades.
• Many search systems you still use are Boolean:
– Email, library catalog, Mac OS X Spotlight
36
Query optimization
•
•
Consider a query that is an and of n terms, n > 2
For each of the terms, get its postings list, then and them
together
•
Example query: BRUTUS AND CALPURNIA AND
CAESAR
•
37
What is the best order for processing this query?
37
Query optimization
•
Example query: BRUTUS AND CALPURNIA AND
CAESAR
•
•
•
38
Simple and effective optimization: Process in order of
increasing frequency
Start with the shortest postings list, then keep cutting
further
In this example, first CAESAR, then CALPURNIA,
then BRUTUS
38
Optimized intersection algorithm for
conjunctive queries
39
39
More general optimization
•
•
•
•
40
Example query: (MADDING OR CROWD)
and (IGNOBLE OR STRIFE)
Get frequencies for all terms
Estimate the size of each or by the sum of its
frequencies (conservative)
Process in increasing order of or sizes
40
Scaling
• These basic techniques are pretty simple
• There are challenges
– Scaling
• as everything becomes digitized, how well do
the processes scale?
– Intelligent information extraction
• I want information, not just a link to a place that
might have that information.
How much information is there?
Yotta
• Soon most everything will be
Everything
recorded and indexed
Recorded !
• Most bytes will never be seen
by humans.
• Data summarization,
All Books
trend detection
MultiMedia
Gray
Microsoft
anomaly detection
are key technologies
All books
See Mike Lesk:
(words)
How much information is
there:
http://www.lesk.com/mlesk/ksg97/ksg.ht
ml
See Lyman & Varian:
How much information
Zetta
Exa
Peta
Tera
A movie
A Photo
http://www.sims.berkeley.edu/research/projects/howmuch-info/
Mega
24 Yecto, 21 zepto, 18 atto, 15 femto, 12 pico, 9 nano, 6 micro, 3 milli
Slide source: Lee Giles (modified)
Giga
Kilo
A Book
Getting the required information
• Dependent on
– Acquiring information
– Storing information
– Indexing
– Interaction with the information source
– Evaluation
Getting the required information
• We have learned about
– Acquiring information
– Storing information
– Indexing (the basics)
– Interaction with the information source
– Evaluation
Getting the required information
• Still to come
– Acquiring information
– Storing information
– Indexing (more)
– Interaction with the information source
– Evaluation
The web and its challenges
 Unusual and diverse documents
 Unusual and diverse users, queries,
information needs
 Beyond terms, exploit ideas from social
networks
 link analysis, clickstreams …
 How do search engines work? And how can
we make them better?
46
References
•
Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze,
Introduction to Information Retrieval, Cambridge University Press. 2008.
–
–
•
•
Book available online at http://nlp.stanford.edu/IR-book/information-retrieval-book.html
Many of these slides are taken directly from the authors’ slides from the first chapter of the book.
C. Lee Giles, Penn State. http://clgiles.ist.psu.edu/
Paging figure from Vittore Carsarosa, University of Parma, Italy