Transcript Information Retrieval Introduction

Instructor : Marina Gavrilova
Outline
 Information Retrieval
 IR vs DBMS
 Boolean Text Search
 Text Indexes
Simple relational text index
 Example of inverted file
Computing Relevance
Vector Space Model
Text Clustering
Probabilistic Models and Ranking Principles
Iterative query refinement - Racchio Model
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Query Modification
 Collaborative Filtering and Ringo Collaborative Filtering
 Conclusions
Goal
 Goal of this lecture is to introduce you to
Informational retrieval and how it differentiates from
DBMS.
 Then we will discuss how vector space model and
Text Clustering help in computing relevance and
similarity between documents.
Information Retrieval
 A research field traditionally separate from Databases
 Goes back to IBM, Rand and Lockheed in the 50’s
 G. Salton at Cornell in the 60’s
 Lots of research since then
 Products traditionally separate
 Originally, document management systems for libraries,
government, law, etc.
 Gained prominence in recent years due to web search
IR vs. DBMS
 Seem like very different beasts:
IR
DBMS
Imprecise Semantics
Precise Semantics
Keyword search
SQL
Unstructured data format
Structured data
Read-Mostly. Add docs
occasionally
Expect reasonable number of
updates
Page through top k results
Generate full answer
 Both support queries over large datasets, use indexing.
 In practice, you currently have to choose between the two.
IR’s “Bag of Words” Model
 Typical IR data model:
 Each document is just a bag (multiset) of words (“terms”)
 Detail 1: “Stop Words”
 Certain words are considered irrelevant and not placed in the bag
 e.g., “the”
 e.g., HTML tags like <H1>
 Detail 2: “Stemming” and other content analysis
 Using English-specific rules, convert words to their basic form
 e.g., “surfing”, “surfed” --> “surf”
Boolean Text Search
 Find all documents that match a Boolean containment
expression:
“Windows”
AND (“Glass” OR “Door”)
AND NOT “Microsoft”
 Note: Query terms are also filtered via stemming and
stop words.
 When web search engines say “10,000 documents
found”, that’s the Boolean search result size (subject to
a common “max # returned’ cutoff).
Text “Indexes”
 When IR says “text index”…
 Usually mean more than what DB people mean
 Both “tables” and indexes
 Really a logical schema (i.e., tables)
 With a physical schema (i.e., indexes)
 Usually not stored in a DBMS
A Simple Relational Text Index
 Create and populate a table
InvertedFile(term string, docURL string)
 Build a B+-tree or Hash index on InvertedFile.term
 <Key, list of URLs> as entries in index critical here for efficient
storage!!
 Fancy list compression possible
 Note: URL instead of RID, the web is your “heap file”!
 Can also cache pages and use RIDs
 This is often called an “inverted file” or “inverted index”
 Maps from words -> docs
 Can now do single-word text search queries!
An Inverted File
 Search for
 “databases”
term
data
database
date
day
dbms
decision
demonstrate
description
design
desire
developer
differ
disability
discussion
division
do
document
docURL
http://www-inst.eecs.berkeley.edu/~cs186
http://www-inst.eecs.berkeley.edu/~cs186
http://www-inst.eecs.berkeley.edu/~cs186
http://www-inst.eecs.berkeley.edu/~cs186
http://www-inst.eecs.berkeley.edu/~cs186
http://www-inst.eecs.berkeley.edu/~cs186
http://www-inst.eecs.berkeley.edu/~cs186
http://www-inst.eecs.berkeley.edu/~cs186
http://www-inst.eecs.berkeley.edu/~cs186
http://www-inst.eecs.berkeley.edu/~cs186
http://www.microsoft.com
http://www-inst.eecs.berkeley.edu/~cs186
http://www.microsoft.com
http://www-inst.eecs.berkeley.edu/~cs186
http://www-inst.eecs.berkeley.edu/~cs186
http://www-inst.eecs.berkeley.edu/~cs186
http://www-inst.eecs.berkeley.edu/~cs186
Computing Relevance, Similarity:
The Vector Space Model
Document Vectors
 Documents are represented as “bags of words”
 Represented as vectors when used computationally
 A vector is like an array of floating point
 Has direction and magnitude
 Each vector holds a place for every term in the collection
 Therefore, most vectors are sparse
Document Vectors:
One location for each word.
nova
10
A
5
B
C
D
E
F
G 5
H
I
galaxy heat
5
3
10
h’wood film
role
diet
fur
“Galaxy” occurs 5 times in text A 10
“Heat” occurs 3 times in text A 9
7 means 0 occurrences.)
9
(Blank
10
10
10
8
7
10 in text
5
“Nova” occurs9 10 times
A
6
10
2
7
8
5
1
3
Document Vectors
Document ids
nova
10
A
5
B
C
D
E
F
G 5
H
I
galaxy heat
5
3
10
h’wood film
10
9
6
7
10
8
10
role
diet
fur
10
9
10
10
1
3
7
5
9
2
7
8
5
We Can Plot the Vectors
Star
Doc about movie stars
Doc about astronomy
Doc about mammal behavior
Diet
Assumption: Documents that are “close” in space are similar.
Vector Space Model
 Documents are represented as vectors in term space
 Terms are usually stems
 Documents represented by binary vectors of terms
 Queries represented the same as documents
 A vector distance measure between the query and
documents is used to rank retrieved documents
 Query and Document similarity is based on length
and direction of their vectors
 Vector operations to capture boolean query conditions
 Terms in a vector can be “weighted” in many ways
Vector Space Documents
and Queries
docs
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
D11
Q
t1
1
1
0
1
1
1
0
0
0
0
1
1
q1
t2
0
0
1
0
1
1
1
1
0
1
0
2
q2
t3
1
0
1
0
1
0
0
0
1
1
1
3
q3
RSV=Q.Di
4
1
5
1
6
3
2
2
3
5
3
Q is a query – also represented
as a vector
t1
t3
D9
D2
D1
D4
D11
D5
D3
D6
D10
D7
D8
t2
Boolean term combinations
Assigning Weights to Terms
Binary Weights
Raw term frequency
 Want to weight terms highly if they are
 frequent in relevant documents … BUT
 infrequent in the collection as a whole
Binary Weights
 Only the presence (1) or absence (0) of a term is
included in the vector
docs
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
D11
t1
1
1
0
1
1
1
0
0
0
0
1
t2
0
0
1
0
1
1
1
1
0
1
0
t3
1
0
1
0
1
0
0
0
1
1
1
Raw Term Weights
 The frequency of occurrence for the term in each
document is included in the vector
docs
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
D11
t1
2
1
0
3
1
3
0
0
0
0
4
t2
0
0
4
0
6
5
8
10
0
3
0
t3
3
0
7
0
3
0
0
0
1
5
1
TF x IDF Normalization
 Normalize the term weights (so longer documents
are not unfairly given more weight)
 The longer the document, the more likely it is for a
given term to appear in it, and the more often a given
term is likely to appear in it. So, we want to reduce
the importance attached to a term appearing in a
document based on the length of the document.
wik 
tf ik log(N / nk )
2
2
(
tf
)
[log(
N
/
n
)]
k 1 ik
k
t
Pair-wise Document Similarity
nova
A
1
5
B
C
D
galaxy
3
2
heat
1
h’wood
film
role
2
4
1
1
5
diet
How to compute document similarity?
fur
Pair-wise Document Similarity
sim( A, B)  (1  5)  (2  3)  11
sim( A, C )  0
D1  w11 , w12, ..., w1t
D2  w21 , w22, ..., w2t
sim( A, D)  0
sim( B, C )  0
t
sim( D1 , D2 )   w1i  w2i
sim( B, D)  0
sim(C , D)  (2  4)  (1 1)  9
i 1
nova
A
1
5
B
C
D
galaxy heat
3
1
2
h’wood film
2
4
1
1
role
5
diet
fur
Pair-wise Document Similarity
(cosine normalization)
D1  w11 , w12 , ..., w1t
D2  w21 , w22 , ..., w2t
t
sim( D1 , D2 )   w1i  w2i unnormalized
i 1
t
sim( D1 , D2 ) 
w
i 1
t
1i
 w2i
2
(
w
)
 1i 
i 1
t
2
(
w
)
 2i
i 1
cosine normalized
Text Clustering
 Finds overall similarities among groups of documents
 Finds overall similarities among groups of tokens
 Picks out some themes, ignores others
Text Clustering
Clustering is
“The art of finding groups in data.”
-- Kaufmann and Rousseeu
Term 1
Term
2
Problems with Vector Space
 There is no real theoretical basis for the assumption of
a term space
 It is more for visualization than having any real basis
 Most similarity measures work about the same
 Terms are not really orthogonal dimensions
 Terms are not independent of all other terms; remember
our discussion of correlated terms in text
Probabilistic Models
 Rigorous formal model attempts to predict the
probability that a given document will be relevant to a
given query
 Ranks retrieved documents according to this
probability of relevance (Probability Ranking
Principle)
 Relies on accurate estimates of probabilities
Probability Ranking Principle
 If a reference retrieval system’s response to each request is a
ranking of the documents in the collections in the order of
decreasing probability of usefulness to the user who
submitted the request, where the probabilities are
estimated as accurately as possible on the basis of whatever
data has been made available to the system for this
purpose, then the overall effectiveness of the system to its
users will be the best that is obtainable on the basis of that
data.
Stephen E. Robertson, J. Documentation 1977
Query Modification
 Problem: How can we reformulate the query to help
a user who is trying several searches to get at the
same information?
 Thesaurus expansion:
Suggest terms similar to query terms
 Relevance feedback:
 Suggest terms (and documents) similar to
retrieved documents that have been judged to be
relevant

Relevance Feedback
 Main Idea:
 Modify existing query based on relevance judgements
 Extract terms from relevant documents and add
them to the query
 AND/OR re-weight the terms already in the query
 There are many variations:
 Usually positive weights for terms from relevant docs
 Sometimes negative weights for terms from nonrelevant docs
 Users, or the system, guide this process by selecting
terms from an automatically-generated list.
Rocchio Method
 Rocchio automatically
 Re-weights terms
 Adds in new terms (from relevant docs)
 have to be careful when using negative terms
 Rocchio is not a machine learning algorithm
Rocchio Method
Q1   Q0 

n1

n1
n2
R  n 
i 1
i
2 i 1
Si
where
Q0  the vectorfor theinitialquery
Ri  the vectorfor therelevantdocumenti
Si  the vectorfor thenon - relevantdocumenti
n1  thenumber of relevantdocumentschosen
n2  thenumber of non - relevantdocumentschosen
 ,  and  tune theimportanceof relevantand nonrelevant terms
(in somestudies best toset  to 0.75and  to 0.25)
Alternative Notions of Relevance Feedback
 Find people whose taste is “similar” to yours.
 Will you like what they like?
 Follow a user’s actions in the background.
 Can this be used to predict what the user will want to see
next?
 Track what lots of people are doing.
 Does this implicitly indicate what they think is good and
not good?
Collaborative Filtering (Social Filtering)
 If Pam liked the paper, I’ll like the paper
 If you liked Star Wars, you’ll like Independence Day
 Rating based on ratings of similar people
 Ignores text, so also works on sound, pictures etc.
 But: Initial users can bias ratings of future users
Star Wars
Jurassic Park
Terminator II
Independence Day
Sally
7
6
3
7
Bob
7
4
4
7
Chris
3
7
7
2
Lynn
4
4
6
2
Karen
7
4
3
?
Ringo Collaborative Filtering
 Users rate items from like to dislike
 7 = like; 4 = ambivalent; 1 = dislike
 A normal distribution; the extremes are what matter
 Nearest Neighbors Strategy: Find similar users and predicted
(weighted) average of user ratings
 Pearson Algorithm: Weight by degree of correlation between user
U and user J
 1 means similar, 0 means no correlation, -1 dissimilar
 Works better to compare against the ambivalent rating (4),
rather than the individual’s average score
rUJ 
 (U  U )(J  J )
 (U  U )   ( J  J )
2
2
Computing Relevance
 Relevance calculation involves how often search terms appear in doc, and how
often they appear in collection:
 More search terms found in doc  doc is more relevant
 Greater importance attached to finding rare terms
 Doing this efficiently in current SQL engines is not easy:
 “Relevance of a doc wrt a search term” is a function that is called once per doc
the term appears in (docs found via inv. index):
For efficient fn computation, for each term, we can store the # times it
appears in each doc, as well as the # docs it appears in.
 Must also sort retrieved docs by their relevance value.
 Also, think about Boolean operators (if the search has multiple terms) and
how they affect the relevance computation!
 An object-relational or object-oriented DBMS with good support for
function calls is better, but you still have long execution path-lengths
compared to optimized search engines.

Updates and Text Search
 Text search engines are designed to be query-mostly:
 Deletes and modifications are rare
 Can postpone updates (nobody notices, no transactions!)

Updates done in batch (rebuild the index)
 Can’t afford to go off-line for an update?


Create a 2nd index on a separate machine
Replace the 1st index with the 2nd!
 So no concurrency control problems
 Can compress to search-friendly, update-unfriendly format
 Main reason why text search engines and DBMSs are
usually separate products.
 Also, text-search engines tune that one SQL query to death!
DBMS vs. Search Engine Architecture
DBMS
Query Optimization
and Execution
{
Search Engine
Search String Modifier
Relational Operators
Ranking Algorithm
“The Query”
Files and Access Methods
The Access Method
Buffer Management
Disk Space Management
Concurrency
and
Recovery
Needed
Buffer ManagementOS
Disk Space Management
}
Simple
DBMS
IR vs. DBMS Revisited
 Semantic Guarantees
 DBMS guarantees transactional semantics
If inserting Xact commits, a later query will see the update
 Handles multiple concurrent updates correctly
 IR systems do not do this; nobody notices!
 Postpone insertions until convenient
 No model of correct concurrency

 Data Modeling & Query Complexity
 DBMS supports any schema & queries
Requires you to define schema
 Complex query language hard to learn
 IR supports only one schema & query
 No schema design required (unstructured text)
 Trivial to learn query language

Lots More in IR …
 How to “rank” the output? I.e., how to compute relevance of
each result item w.r.t. the query?
 Doing this well / efficiently is hard!
 Other ways to help users paw through the output?
 Document “clustering”, document visualization
 How to take advantage of hyperlinks?
 Really cute tricks here!
 How to use compression for better I/O performance?
 E.g., making RID lists smaller
 Try to make things fit in RAM!
 How to deal with synonyms, misspelling, abbreviations?
 How to write a good web crawler?
Summary
 First we studied difference between Information
Retrieval and DBMS . Then we disused on two type of
searches (Boolean and Text based search) used in
DBMS .
 In addition, we learned how we can compute relevance
between documents based on words using Vector Space
Model and how text clustering can be used to find
similarity between documents and in the end we
discussed Racchio Model for iterative query refinement.
Summary
 IR relies on computing distance between documents
 Terms can be weighted and distances normalized
 IR can utilize clustering, adaptive query updates and
elements of learning to perform document retrieval /
response to query better
 Idea is to use not only similarity, but dissimilarity
measures to compare documents.