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The Vector Space Model
LBSC 796/CMSC828o
Session 3, February 9, 2004
Douglas W. Oard
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Agenda
•T= hinking about search
–D= esign strategies
–D= ecomposing the search component
•
•B= oolean “free text” retrieval
–T= he “bag of terms” representation
–P= roximity operators
•
•R= anked retrieval
–V= ector space model
–P= assage retrieval
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Supporting the Search Process
Source
Selection
Search
Query
Selection
Ranked List
Examination
Document
Delivery
Document
Query
Formulation
IR System
Indexing
Index
Acquisition
Collection
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charset="us-ascii" Boolean Retrieval Model and Controlled Vocabulary Techniques
Design Strategies
&#= 8226;Foster human-machine synergy
&#= 8211;Exploit complementary strengths
&#= 8211;Accommodate shared weaknesses
– <= /div>
&#= 8226;Divide-and-conquer
&= #8211;Divide task into stages with well-defined interfaces
&= #8211;Continue dividing until problems are easily solved
– <= /div>
&#= 8226;Co-design related components
&#= 8211;Iterative process of joint optimization
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Human-Machine Synergy
&#= 8226;Machines are good at:
&= #8211;Doing simple things accurately and quickly
&= #8211;Scaling to larger collections in sublinear time
• <= /div>
&#= 8226;People are better at:
&= #8211;Accurately recognizing what they are looking for
&= #8211;Evaluating intangibles such as “quality”
– <= /div>
&#= 8226;Both are pretty bad at:
&= #8211;Mapping consistently between words and concepts
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Divide and Conquer
&#= 8226;Strategy: use encapsulation to limit complexity
• <= /div>
&#= 8226;Approach:
&= #8211;Define interfaces (input and output) for each component =
&= #8226;Query interface: input terms, output representation
&= #8211;Define the functions performed by each component
&= #8226;Remove common words, weight rare terms higher, …
&= #8211;Repeat the process within components as needed
• <= /div>
&#= 8226;Result: a hierarchical decomposition
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Search Goal
&#= 8226;Choose the same documents a human would
&= #8211;Without human intervention (less work)
&= #8211;Faster than a human could (less time)
&= #8211;As accurately as possible (less accuracy)
&#= 8226;Humans start with an information need
&= #8211;Machines start with a query
&#= 8226;Humans match documents to information needs
&#= 8211;Machines match document & query representations
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Search Component Model
Comparison Function
Representation Function
Query Formulation
Human Judgment
Representation Function<= /div>
Retrieval Status Value
Utility
Query=
Information Need
Docum= ent
Query Representation
Document Representation
3D"Text=
Query  Processing
3D"Text
Document  Processing
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Relevance
&#= 8226;Relevance relates a topic and a document
&= #8211;Duplicates are equally relevant, by definition
&= #8211;Constant over time and across users
&#= 8226;Pertinence relates a task and a document
&= #8211;Accounts for quality, complexity, language, …
&#= 8226;Utility relates a user and a document
&= #8211;Accounts for prior knowledge
&#= 8226;We seek utility, but relevance is what we get!
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“Bag of Terms” Representation
•Bag =3D a “set” that can contain dupli= cates
<= span style=3D'font-size:86%'>Ø“The quick brown fox jumped over the lazy dog’s back” ®
•   <= /span> {back, brown, dog, fox, jump, lazy, over, quick, the, the}
•
•Vector =3D values recorded in any consistent order=
<= span style=3D'font-size:86%'>Ø{back, brown, dog, fox, jump, lazy, over, quick= , the, the} ®
– [1 1 1 1 1 1 1 1 2]
Star= t 1:30
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Bag of Terms Example
The quick brown
fox jumped over
the lazy dog’s
back.
Document 1
Document 2
Now is the time
for all good men
to come to the
aid of their party.
the
quick
brown
fox
over
lazy
dog
back
now
is
time
for
all
good
men
to
come
jump
aid
of
their
party
0
0
1
1
0
1
1
0
1
1
0
0
1
0
1
0
0
1
1
0
0
1
0
0
1
0
0
1
1
0
1
0
1
1
  Term
3D"Text
Document 1
3D"Text
Document 2
Stopword
     List
Star= t:  1:35

Ques= tions:

What= is the consistent order in this case?
Why = is a consistent order needed?

How = are the terms chosen in this case?
Stop= word lists eliminate terms  that only c= onvey meaning through word order

Obse= rvations:

No n= eed to keep a position for terms that never occur
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Boolean “Free Text” Retrieval
&#= 8226;Limit the bag of words to “absent” and “present”
&= #8211;“Boolean” values, represented as 0 and 1
&#= 8226;Represent terms as a “bag of documents”
&= #8211;Same representation, but rows rather than columns
&#= 8226;Combine the rows using “Boolean operators”
&= #8211;AND, OR, NOT
&#= 8226;Result set: every document with a 1 remaining
Star= t 1:40
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Boolean Operators
0
1
1
1
0
1
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1
A&n= bsp; OR  B
A&n= bsp; AND  B
A&= nbsp; NOT  B
A
B
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(= =3D A  AND NOT  B)
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Boolean Free Text Example
quick
brown
fox
over
lazy
dog
back
now
time
all
good
men
come
jump
aid
their
party
0
0
1
1
0
0
0
0
0
1
0
0
1
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1
0
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1
0
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1
Term
3D"Text
Doc 1
3D"Text
Doc 2
0
0
1
1
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1
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1
3D"Text
Doc 3
3D"Text
Doc 4
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3D"Text
Doc 5
3D"Text
Doc 6
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3D"Text
Doc 7
3D"Text
Doc 8
•d= og AND fox 
–D= oc 3, Doc 5
•d= og NOT fox 
–E= mpty
•f= ox NOT dog 
–D= oc 7
•d= og OR fox     <= /span>
–D= oc 3, Doc 5, Doc 7
•g= ood AND party
–D= oc 6, Doc 8
•g= ood AND party NOT over
–Doc 6
Star= t 1:50
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------=_NextPart_01C3EDAD.E7DF92E0 Content-Location: file:///C:/3578CE33/7960203_files/slide0086_image238.gif Content-Transfer-Encoding: base64 Content-Type: image/gif R0lGODlhFQApAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAYABwAK ABwAhAAAAAAAAAgFAA0LCwUICw0IBQAABQgIDQsIBQgICAUAAAUIDQgFCAsLDQsICAsFAAAFBQUF CwAFCAAFCwgICwUABQ0ICAgFBQsFBQECAwECAwECAwECAwECAwECAwECAwVv4CAEBFAExgEgKpIo y8A0TgOIJVCzwANFt5koIGmcAguAcslsOp9Q6GNiQi4HlGvFFuQWikuLIoDiRq9jonlwUQEsQCWW WZPPlF+zmAw+Kx9kanICOW8VKgWFSnA6ZksYNI5KNZBONYCBgQaRlg0hADs= ------=_NextPart_01C3EDAD.E7DF92E0 Content-Location: file:///C:/3578CE33/7960203_files/slide0087.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Boolean Retrieval Model and Controlled Vocabulary Techniques
Why Boolean Retrieval Works
= 226;Boolean operators approximate natural language
&= #8211;Find documents about a good party that is not over
&#= 8226;AND can discover relationships between concepts
&= #8211;good party
= 226;OR can discover alternate terminology
&= #8211;excellent party
= 226;NOT can discover alternate meanings
&= #8211;Democratic party
Star= t 1:55
------=_NextPart_01C3EDAD.E7DF92E0 Content-Location: file:///C:/3578CE33/7960203_files/slide0088.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Boolean Retrieval Model and Controlled Vocabulary Techniques
The Perfect Query Paradox
&#= 8226;Every information need has a perfect doc set
&= #8211;If not, there would be no sense doing retrieval
&#= 8226;Almost every document set has a perfect query
&= #8211;AND every word to get a query for document 1
&= #8211;Repeat for each document in the set
&= #8211;OR every document query to get the set query
&#= 8226;But users find Boolean query formulation hard
&= #8211;They get too much, too little, useless stuff, …
Star= t 2:00
------=_NextPart_01C3EDAD.E7DF92E0 Content-Location: file:///C:/3578CE33/7960203_files/slide0089.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Boolean Retrieval Model and Controlled Vocabulary Techniques
Why Boolean Retrieval Fails
&#= 8226;Natural language is way more complex
&= #8211;She saw the man on the hill with a telescope
&#= 8226;AND “discovers” nonexistent relationships
&= #8211;Terms in different paragraphs, chapters, …
&#= 8226;Guessing terminology for OR is hard
&= #8211;good, nice, excellent, outstanding, awesome, …
&#= 8226;Guessing terms to exclude is even harder!
&= #8211;Democratic party, party to a lawsuit, …
Star= t 2:10

End = at 2:15 with the break
------=_NextPart_01C3EDAD.E7DF92E0 Content-Location: file:///C:/3578CE33/7960203_files/slide0090.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Boolean Retrieval Model and Controlled Vocabulary Techniques
Proximity Operators
&#= 8226;More precise versions of AND
&= #8211;“NEAR n” allows at most n-1 intervening terms
&= #8211;“WITH” requires terms to be adjacent and in order
&#= 8226;Easy to implement, but less efficient
&= #8211;Store a list of positions for each word in each doc
&= #8226;Stopwords become very important!
&= #8211;Perform normal Boolean computations
&= #8226;Treat WITH and NEAR like AND with an extra constraint
Star= t 2:30 after the break
------=_NextPart_01C3EDAD.E7DF92E0 Content-Location: file:///C:/3578CE33/7960203_files/slide0091.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Boolean Retrieval Model and Controlled Vocabulary Techniques
Proximity Operator Example
= 226;time AND come
&= #8211;Doc 2
&#= 8226;time (NEAR 2) come
&= #8211;Empty
&#= 8226;quick (NEAR 2) fox
&= #8211;Doc 1
= 226;quick WITH fox
&= #8211;Empty
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Doc 1
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Doc 2
Star= t 2:35
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RqIYwnICMtOs9i07LcHjhtsRj8ikckkUmA6BHg7im50SkZZLdZNMSDAmMfENcK8NyklS8VFvFi1W ezgDvCQ7c5SXX4ZrCmoHgF1tcUgYBohHcYpIcXwkkzCMRHEhADs= ------=_NextPart_01C3EDAD.E7DF92E0 Content-Location: file:///C:/3578CE33/7960203_files/slide0039.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Boolean Retrieval Model and Controlled Vocabulary Techniques
Strengths and Weaknesses
&#= 8226;Strong points
&= #8211;Accurate, if you know the right strategies
&= #8211;Efficient for the computer
&#= 8226;Weaknesses
&= #8211;Often results in too many documents, or none
&= #8211;Users must learn Boolean logic
&= #8211;Sometimes finds relationships that don’t exist
&= #8211;Words can have many meanings
&= #8211;Choosing the right words is sometimes hard
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Ranked Retrieval Paradigm
&#= 8226;Exact match retrieval often gives useless sets
&= #8211;No documents at all, or way too many documents
&#= 8226;Query reformulation is one “solution”
&= #8211;Manually add or delete query terms
&#= 8226;“Best-first” ranking can be superior
&= #8211;Select every document within reason
&#= 8211;Put them in order, with the “best” ones first
&= #8211;Display them one screen at a time
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Advantages of Ranked Retrieval
&#= 8226;Closer to the way people think
&= #8211;Some documents are better than others
• <= /div>
&#= 8226;Enriches browsing behavior
&= #8211;Decide how far down the list to go as you read it
• <= /div>
&#= 8226;Allows more flexible queries
&= #8211;Long and short queries can produce useful results
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Ranked Retrieval Challenges
&#= 8226;“Best first” is easy to say but hard to do!
&= #8211;The best we can hope for is to approximate it
• <= /div>
&#= 8226;Will the user understand the process?
&= #8211;It is hard to use a tool that you don’t understand
• <= /div>
&#= 8226;Efficiency becomes a concern
&= #8211;Only a problem for long queries, though
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Partial-Match Ranking
&#= 8226;Form several result sets from one long query
&= #8211;Query for the first set is the AND of all the terms
&= #8211;Then all but the 1st term, all but the 2nd, …
&= #8211;Then all but the first two terms, …
&= #8211;And so on until each single term query is tried
= 226;Remove duplicates from subsequent sets
&#= 8226;Display the sets in the order they were made
&= #8211;Document rank within a set is arbitrary
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Partial Match Example
information AND retrieval
Readings in Information Retrieval
Information Storage and Retrieval
Speech-Based Information Retrieval for Digital Libraries
Word Sense Disambiguation and Information Retrieva= l
information NOT retrieval
The S= tate of the Art in Information Filtering
Inference Networks for Document Retrieval
Content-Based Image Retrieval Systems <= /div>
Video Parsing, Retrieval and Browsing <= /div>
An Approach to Conceptual Text Retrieval Using the EuroWordNet …
Cross-Language Retrieval:  English/Russian/French
retrieval NOT information
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Similarity-Based Queries
&#= 8226;Treat the query as if it were a document
&= #8211;Create a query bag-of-words
&#= 8226;Find the similarity of each document
&= #8211;Using the coordination measure, for example
&#= 8226;Rank order the documents by similarity
&= #8211;Most similar to the query first
&#= 8226;Surprisingly, this works pretty well!
&= #8211;Especially for very short queries
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Document Similarity
&#= 8226;How similar are two documents?
&= #8211;In particular, how similar is their bag of words?
1
1
1
1: Nuclear fallout contaminated Monta= na.
2: Information retrieval is interesti= ng.
3: Information retrieval is complica= ted.
1
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1
nuclear=
fallout=
siberia=
contam= inated
intere= sting
compli= cated
inform= ation
retriev= al
1
1
2
3
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The Coordination Measure
&#= 8226;Count the number of terms in common
&= #8211;Based on Boolean bag-of-words
&#= 8226;Documents 2 and 3 share two common terms
&= #8211;But documents 1 and 2 share no terms at all
&#= 8226;Useful for “more like this” queries
&= #8211;“more like doc 2” would rank doc 3 ahead of doc 1
&#= 8226;Where have you seen this before?
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Coordination Measure Example
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nuclear<= /span>
fallout<= /span>
siberia<= /span>
contami= nated
interes= ting
complic= ated
informa= tion
retrieva= l
1
1=
2=
3
Query:  complicated retrieval
Result:  3, 2
Query:  information retrieval
Result:  2, 3
Query: interesting nuclear fallout
Result:  1, 2
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Counting Terms
&#= 8226;Terms tell us about documents
&= #8211;If “rabbit” appears a lot, it may be about rabbits
&#= 8226;Documents tell us about terms
&= #8211;“the” is in every document -- not discriminating
= 226;Documents are most likely described well by rare terms that occur in = them frequently
&= #8211;Higher “term frequency” is stronger evidence
&#= 8211;Low “collection frequency” makes it stronger still
Cont= rast this with Boolean retrieval

1) T= F was always 1 or zero
2) S= earchers mad to consider collection frequency
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The Document Length Effect
&#= 8226;Humans look for documents with useful parts
&= #8211;But probabilities are computed for the whole
– <= /div>
&#= 8226;Document lengths vary in many collections
&= #8211;So probability calculations could be inconsistent
– <= /div>
&#= 8226;Two strategies
&= #8211;Adjust probability estimates for document length
&= #8211;Divide the documents into equal “passages”
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Incorporating Term Frequency
&#= 8226;High term frequency is evidence of meaning
&= #8211;And high IDF is evidence of term importance
&#= 8226;Recompute the bag-of-words
&= #8211;Compute TF * IDF for every element
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Weighted Matching Schemes
&#= 8226;Unweighted queries
&= #8211;Add up the weights for every matching term
&#= 8226;User specified query term weights
&= #8211;For each term, multiply the query and doc weights
&= #8211;Then add up those values
&#= 8226;Automatically computed query term weights
&= #8211;Most queries lack useful TF, but IDF may be useful
&= #8211;Used just like user-specified query term weights
------=_NextPart_01C3EDAD.E7DF92E0 Content-Location: file:///C:/3578CE33/7960203_files/slide0056.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Boolean Retrieval Model and Controlled Vocabulary Techniques
TF*IDF Example
4
5
6
3
1
3
1
6
5
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1
nuclear<= /span>
fallout<= /span>
siberia<= /span>
contami= nated
interes= ting
complic= ated
informa= tion
retrieva= l
2
1
2
3
2
3
2
4
4
0.50
0.63
0.90
0.13
0.60
0.75
1.51
0.38
0.50
2.11
0.13
1.20
1
2
3
0.60
0.38
0.50
4
0.301
0.125
0.125
0.125
0.602
0.301
0.000
0.602
Unweighted query:
  contaminated retrieval
Result: 2, 3, 1, 4
Weighted query:
  contaminated(3) retrieval(1)
Result: 1, 3, 2, 4
IDF-weighted query:
  contaminated retrieval
Result: 2, 3, 1, 4
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R0lGODlhLgAqAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAUADQAj ABYAhAAAAAAAAAgFAA0LCwUIDQ0ICAUFCwsFAAUICwAFCw0IBQAFCAsLDQsIBQgIBQUAAAsFBQgI DQAABQgICwgFBQUFCAUABQsICAECAwECAwECAwECAwECAwECAwECAwECAwWvIDAIAQGcQGGgR4m+ cNwGAfIeCRvkcS/vKEVgwUA1bL7kSUgUOR7NEySiVBYekmJhIsieVFXlAMsYTEbeAaUY9o0lkUIE XTy2k+jJCnCAFypsdzFogCd9ExRUgj0jNUY0Ji8NXkp9bCM8Jw1Ai26JLwpRnaMnYy5JLaKCaoE+ CkidYFVTpHZWhZ1qikm2uRYXu4MCkZ1CmVeUKRatpC9yRrDNKGMJAxZxy9JuD0PMIQA7 ------=_NextPart_01C3EDAD.E7DF92E0 Content-Location: file:///C:/3578CE33/7960203_files/slide0057.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Boolean Retrieval Model and Controlled Vocabulary Techniques
Document Length Normalization
&#= 8226;Long documents have an unfair advantage
&= #8211;They use a lot of terms
&= #8226;So they get more matches than short documents
&= #8211;And they use the same words repeatedly
&= #8226;So they have much higher term frequencies
&#= 8226;Normalization seeks to remove these effects
&= #8211;Related somehow to maximum term frequency
&= #8211;But also sensitive to the of number of terms
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“Cosine” Normalization
&#= 8226;Compute the length of each document vector
&= #8211;Multiply each weight by itself
&= #8211;Add all the resulting values
&= #8211;Take the square root of that sum
&#= 8226;Divide each weight by that length
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Cosine Normalization Example
0.29
0.37
0.53
<= span style=3D'font-size:58%;mso-special-format:lastCR;display:none'>
0.13
0.62
0.77
0.57
0.14
0.19
0.79
0.05
0.71
1
2
3
0.69
0.44
0.57
4=
4
5
6
3
1
3
1
6
5
3
4
3
7
1
nuclear<= /span>
fallout<= /span>
siberia<= /span>
contami= nated
interes= ting
complic= ated
informa= tion
retrieva= l
2
1
2
3
2
3
2
4
4
0.50
0.63
0.90
0.13
0.60
0.75
1.51
0.38
0.50
2.11
0.13
1.20
1
2
3
0.60
0.38
0.50
4
0.301
0.125
0.125
0.125
0.602
0.301
0.000
0.602
1.70
0.97
2.67
0.87
Length=
Unwei= ghted query: contaminated retrieval,  Result: 2, 4, 1, 3 (compare to 2, 3, 1, 4)
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Why Call It “Cosine”?
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file:///C:/3578CE33/7960203_files/slide0061.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Boolean Retrieval Model and Controlled Vocabulary Techniques
Interpreting the Cosine Measure
= 226;Think of a document as a vector from zero
&#= 8226;Similarity is the angle between two vectors
&#= 8211;Small angle =3D very similar
&#= 8211;Large angle =3D little similarity
&#= 8226;Passes some key sanity checks
&= #8211;Depends on pattern of word use but not on length
&#= 8211;Every document is most similar to itself
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“Okapi” Term Weights
TF component
IDF component
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Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Boolean Retrieval Model and Controlled Vocabulary Techniques
Passage Retrieval
&#= 8226;Another approach to long-document problem
&= #8211;Break it up into coherent units
• <= /div>
&#= 8226;Recognizing topic boundaries is hard
&#= 8211;But overlapping 300 word passages work fine
• <= /div>
&#= 8226;Document rank is best passage rank
&= #8211;And passage information can help guide browsing
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Summary
= 226;Goal: find documents most similar to the query
– <= /div>
&#= 8226;Compute normalized document term weights
&= #8211;Some combination of TF, DF, and Length
– <= /div>
&#= 8226;Optionally, get query term weights from the user
&= #8211;Estimate of term importance
– <= /div>
&#= 8226;Compute inner product of query and doc vectors
&= #8211;Multiply corresponding elements and then add
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Before You Go!
•   On a sheet of paper, please briefly answer the following question (no names):
•
•   What was the muddiest point= in today’s lecture?
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Click to edit Master text styles
Second level
Third level
Fourth level
Fifth level
2
Start 1:00
16
17
19
5
Start 1:15
6
Start 1:20
8
Start 1:30
9
Start:  1:35
Questions:
What is the consistent order in this case?
Why is a consistent order needed?
How are the terms chosen in this case?
Stopword lists eliminate terms  that only convey meaning through word order
Observations:
No need to keep a position for terms that never occur
10
Start 1:40
11
Start 1:45
12
Start 1:50
13
Start 1:55
14
Start 2:00
15
Start 2:10
End at 2:15 with the break
16
Start 2:30 after the break
17
Start 2:35
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Contrast this with Boolean retrieval
1) TF was always 1 or zero
2) Searchers mad to consider collection frequency
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g_origH=3Dsld.style.posHeight g_origW=3Dsld.style.posWidth g_scaleHyperlinks=3D(document.all.tags("AREA").length>0) if( g_scaleHyperlinks ) InitHLinkArray() if( g_scaleInFrame||(IsWin("PPTSld") && parent.IsFullScrMode() ) ) document.body.scroll=3D"no" _RSW() if( IsWin("PPTSld") && parent.IsFullScrMode() ) FullScrInit(); =09 MakeSldVis(); ChkAutoAdv() if( runAnimations ) { if( document.all("NSPlay") ) document.all("NSPlay").autoStart =3D false; if( sld.filters && sld.filters.revealtrans ) setTimeout( "document.body.start()", sld.filters.revealtrans.duration * = 1000 ); else document.body.start(); } } function MakeSldVis() { var fTrans=3Dg_showAnimation && SldHasTrans() if( fTrans ) { if( g_bgSound ) { idx=3Dg_bgSound.indexOf(","); pptSound.src=3Dg_bgSound.substr( 0, idx ); pptSound.loop=3D -(parseInt(g_bgSound.substr(idx+1))); } SlideObj.filters.revealtrans.Apply() } SlideObj.style.visibility=3D"visible" if( fTrans ) SlideObj.filters.revealtrans.Play() } function MakeNotesVis() { if( 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document.oncontextmenu=3Dparent._CM; document.onkeydown =3D _KDH; document.ondragstart=3DCancel document.onselectstart=3DCancel self.focus() } function Redirect( frmId ) { var str=3Ddocument.location.hash,idx=3Dstr.indexOf('#'), sId=3DGetSldId() if(idx>=3D0) str=3Dstr.substr(1); if( window.name !=3D frmId && ( sId !=3D str) ) { obj =3D GetObj("Main-File") window.location.href=3Dobj.href+"#"+sId return 1 } return 0 } var MHTMLPrefix =3D CalculateMHTMLPrefix(); function CalculateMHTMLPrefix() { if ( document.location.protocol =3D=3D 'mhtml:') { href=3Dnew String(document.location.href) Start=3Dhref.indexOf('!')+1 End=3Dhref.lastIndexOf('/')+1 if (End < Start) return href.substring(0, Start) else return href.substring(0, End) } return ''; } function GetTags(base,tag) { if(g_supportsPPTHTML) return base.all.tags(tag); else return base.getElementsByTagName(tag); } function UpdNtsPane(){ if(frames["PPTNts"]) PPTNts.location.replace( MHTMLP= refix+GetHrefObj( gCurSld ).mNtsHref ) } function UpdNavPane( sldIndex ){ if(gNavLoaded) PPTNav.UpdNav() } function UpdOtNavPane(){ if(gOtlNavLoaded) PPTOtlNav.UpdOtlNav() } function UpdOtlPane(){ if(gOtlLoaded) PPTOtl.UpdOtl() } function SetHasNts( fVal ) { if( gHasNts !=3D fVal ) { gHasNts=3DfVal UpdNavPane() } } function ToggleOtlText() { gOtlTxtExp=3D!gOtlTxtExp UpdOtlPane() } function ToggleOtlPane() { frmset=3Ddocument.all("PPTHorizAdjust") frm=3Ddocument.all("PPTOtl") if( gOtlOpen ) frmset.cols=3D"*,100%" else frmset.cols=3D"25%,*" gOtlOpen=3D!gOtlOpen frm.noResize=3D!frm.noResize UpdOtNavPane() } function ToggleNtsPane() { frmset=3Ddocument.all("PPTVertAdjust") frm=3Ddocument.all("PPTNts") if( gNtsOpen ) frmset.rows=3D"100%,*" else frmset.rows=3D"*,20%" gNtsOpen=3D!gNtsOpen UpdNtsPane() } function ClearMedia() { if (PPTSld.pptSound) PPTSld.pptSound.loop =3D 0; } function FullScreen() { if ( PPTSld.g_animUseRuntime ) PPTSld.document.body.pause(); ClearMedia(); var href =3D ( document.location.protocol =3D=3D 'mhtml:') ? FULLSCR_HREF = : FULLSCR_HREF+"#"+GetHrefObj(gCurSld).mSldHref; if(PPTNav.event.ctrlKey) { var w =3D (window.screen.availWidth * 1.0) / 2.0 var h =3D w * (PPTSld.g_origH * 1.0) / PPTSld.g_origW win =3D window.open( MHTMLPrefix+href,null,"toolbar=3D0,resizable=3D1,top= =3D0,left=3D0," + "width=3D"+ w + ",height=3D" + h ); if( PPTSld.g_animUseRuntime ) win.document.body.PPTSldFrameset=3Dwindow; } else { win =3D window.open( MHTMLPrefix+href,null,"fullscreen=3Dyes" ); if( PPTSld.g_animUseRuntime ) win.document.body.PPTSldFrameset=3Dwindow; } } function ToggleVNarration() { rObj=3DPPTSld.document.all("NSPlay") if( rObj && !PPTSld.g_animUseRuntime ) { if( (rObj.playState =3D=3D 1)||(rObj.playState =3D=3D 0) ) rObj.Play() else if( rObj.playState =3D=3D 2 ) rObj.Pause() else return; } else if( PPTSld.g_animUseRuntime ) { narObj =3D PPTSld.document.all("narrationID") if( narObj ) narObj.togglePause() } } function GetCurSldNum() { obj=3DGetHrefObj(gCurSld) if( obj.mOrigVis =3D=3D 1 ) return obj.mSldIdx else return gCurSld } function GetNumSlds() { if( GetHrefObj(gCurSld).mOrigVis =3D=3D 1 ) return GetSldList().mNumVisSlds; else return GetSldList().mList.length } function GetSldNum( href ) { for(ii=3D0; ii 1 ) PopSldList(); else if( !IsFramesMode() ) { if( gLoopCont ) GoToFirst() else EndShow() } } function GoToPrevSld() { ii=3DgCurSld-1 if( ii > 0 ) { obj=3DGetHrefObj(ii) while ( obj && ( obj.mVis =3D=3D 0 ) && ( ii>0 ) ) obj=3DGetHrefObj(--ii) if( ii =3D=3D 0 ) ii=3D1 GoToSldNum(ii) } } function GoToFirst(){ GoToSld( GetHrefObj(1).mSldHref ) } function GoToLast() { ii=3DGetSldList().mList.length if( ii !=3D gCurSld ) GoToSld( GetHrefObj(ii).mSldHref ) } function GoToSldNum( num ) { if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue obj =3D GetHrefObj( num ) obj.mVis=3D1 gPrevSld=3DgCurSld gCurSld =3D num; PPTSld.location.replace(MHTMLPrefix+obj.mSldHref) if( IsFramesMode() ) { UpdNavPane(); UpdOtlPane(); UpdNtsPane() } } function GoToSld( href ) { if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue GetHrefObj( GetSldNum(href) ).mVis=3D1 PPTSld.location.replace(MHTMLPrefix+href) } function SldUpdated( id ) { if( id =3D=3D GetHrefObj(gCurSld).mSldHref ) return gPrevSld=3DgCurSld gCurSld=3DGetSldNum(id) if( IsFramesMode() ) { UpdNavPane(); UpdOtlPane(); UpdNtsPane() } } function PrevSldViewed(){ GoToSld( GetHrefObj(gPrevSld).mSldHref ) } function HasPrevSld() { return ( gIsEndShow || ( gCurSld !=3D 1 && GetHrefO= bj( gCurSld-1 ).mVis =3D=3D 1 )||( GetCurSldNum() > 1 ) ) } function HasNextSld() { return (GetCurSldNum() !=3D GetNumSlds()) } function CloseWindow() { if( HideMenu() ) return; var event =3D PPTSld.event; if( !IsFramesMode() && event && (event.keyCode=3D=3D27 || event.keyCode=3D= =3D32 || event.type=3D=3D"click" ) ) window.close( self ); CatchNumKeys( self, event ); } function Unload() { gIsEndShow=3D0; } function SetupEndShow() { gIsEndShow=3D1; PPTSld.document.body.scroll=3D"no"; PPTSld.document.onkeypress=3DCloseWindow; PPTSld.document.onclick=3DCloseWindow; PPTSld.document.oncontextmenu=3D_CM; } function EndShow() { if( IsFramesMode() ) return if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue doc=3DPPTSld.document doc.open() doc.writeln('


' + ENDSHOW_MESG + '

') doc.close() } function SetSldVisited(){ GetSldList().mList[gCurSld-1].mVisited=3Dtrue } function IsSldVisited(){ return GetSldList().mList[gCurSld-1].mVisited } function hrefList( sldHref, visible, advDelay, advClk ) { this.mSldHref=3D this.mNtsHref =3D sldHref this.mOrigVis=3D this.mVis =3D visible this.mVisited=3D false this.mAdvDelay=3D advDelay this.mAdvOnClk=3D advClk } function SldList(arr,curSld,fEnd) { this.mCurSld =3D curSld; this.mList =3D new Array(); var idx =3D 1; for(ii=3D0;ii 0) { PushSldList(sldList,fEnd); gCurSld =3D 1; } else if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue } function PushSldList(arr,fEnd) { var ii =3D gSldStack.length; gSldStack[ii] =3D new SldList(arr,gCurSld,fEnd); GoToSld( gSldStack[ii].mList[0].mSldHref ); } function PopSldList() { if (gSldStack[gSldStack.length-1].fEndShow) EndShow() else { gCurSld =3D gSldStack[gSldStack.length-1].mCurSld; gSldStack[gSldStack.length-1] =3D null; gSldStack.length--; var sldList =3D gSldStack[gSldStack.length-1]; GoToSld( sldList.mList[gCurSld - 1].mSldHref ); } } var custShowList=3Dnew Array(); function ImgBtn( oId,bId,w,action ) { var t=3Dthis t.Perform =3D _IBP t.SetActive =3D _IBSetA t.SetInactive=3D _IBSetI t.SetPressed =3D _IBSetP t.SetDisabled=3D _IBSetD t.Enabled =3D _IBSetE t.ChangeIcon =3D null t.UserAction =3D action t.ChgState =3D _IBUI t.mObjId =3D oId t.mBorderId=3D bId t.mWidth =3D w t.mIsOn =3D t.mCurState =3D 0 } function _IBSetA() { if( this.mIsOn ) { obj=3Dthis.ChgState( gHiliteClr,gShadowClr,2 ) obj.style.posTop=3D0 } } function _IBSetI() { if( this.mIsOn ) { obj=3Dthis.ChgState( gFaceClr,gFaceClr,1 ) obj.style.posTop=3D0 } } function _IBSetP() { if( this.mIsOn ) { obj=3Dthis.ChgState( gShadowClr,gHiliteClr,2 ) obj.style.posLeft+=3D1; obj.style.posTop+=3D1 } } function _IBSetD() { obj=3Dthis.ChgState( gFaceClr,gFaceClr,0 ) obj.style.posTop=3D0 } function _IBSetE( state ) { var t=3Dthis GetObj( t.mBorderId ).style.visibility=3D"visible" if( state !=3D t.mIsOn ) { t.mIsOn=3Dstate if( state ) t.SetInactive() else t.SetDisabled() } } function _IBP() { var t=3Dthis if( t.mIsOn ) { if( t.UserAction !=3D null ) t.UserAction() if( t.ChangeIcon ) { obj=3DGetObj(t.mObjId) if( t.ChangeIcon() ) obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-4)*t.mWidth else obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-0)*t.mWidth } t.SetActive() } } function _IBUI( clr1,clr2,nextState ) { var t=3Dthis SetBorder( GetObj( t.mBorderId ),clr1,clr2 ) obj=3DGetObj( t.mObjId ) obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-nextState)*t.mWidth-obj= .style.posTop t.mCurState=3DnextState return obj } function TxtBtn( oId,oeId,action,chkState ) { var t=3Dthis t.Perform =3D _TBP t.SetActive =3D _TBSetA t.SetInactive=3D _TBSetI t.SetPressed =3D _TBSetP t.SetDisabled=3D _TBSetD t.SetEnabled =3D _TBSetE t.GetState =3D chkState t.UserAction =3D action t.ChgState =3D _TBUI t.mObjId =3D oId t.m_elementsId=3D oeId t.mIsOn =3D 1 } function _TBSetA() { var t=3Dthis if( t.mIsOn && !t.GetState() ) t.ChgState( gHiliteClr,gShadowClr,0,0 ) } function _TBSetI() { var t=3Dthis if( t.mIsOn && !t.GetState() ) t.ChgState( gFaceClr,gFaceClr,0,0 ) } function _TBSetP() { if( this.mIsOn ) this.ChgState( gShadowClr,gHiliteClr,1,1 ) } function _TBSetD() { this.ChgState( gFaceClr,gFaceClr,0,0 ) this.mIsOn =3D 0 } function _TBSetE() { var t=3Dthis if( !t.GetState() ) t.ChgState( gFaceClr,gFaceClr,0,0 ) else t.ChgState( gShadowClr,gHiliteClr,1,1 ) t.mIsOn =3D 1 } function _TBP() { var t=3Dthis if( t.mIsOn ) { if( t.UserAction !=3D null ) t.UserAction() if( !t.GetState ) return if( t.GetState() ) t.SetPressed() else t.SetActive() } } function _TBUI( clr1,clr2,lOffset,tOffset ) { SetBorder( GetObj( this.mObjId ),clr1,clr2 ) Offset( GetObj( this.m_elementsId ),lOffset,tOffset ) } function Offset( obj, top, left ){ obj.style.top=3Dtop; obj.style.left=3Dle= ft } function SetBorder( obj, upperLeft, lowerRight ) { s=3Dobj.style; s.borderStyle =3D "solid" s.borderWidth =3D 1 s.borderLeftColor =3D s.borderTopColor =3D upperLeft s.borderBottomColor=3D s.borderRightColor =3D lowerRight } function GetBtnObj(){ return gBtnArr[window.event.srcElement.id] } function BtnOnOver(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetActive() } function BtnOnDown(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetPressed() } function BtnOnOut(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetInactive() } function BtnOnUp() { b=3DGetBtnObj() if( b !=3D null ) b.Perform() else Upd() } function GetNtsState(){ return parent.gNtsOpen } function GetOtlState(){ return parent.gOtlOpen } function GetOtlTxtState(){ return parent.gOtlTxtExp } function NtsBtnSetFlag( fVal ) { s=3Ddocument.all.item( this.m_flagId ).style s.display=3D"none" if( fVal ) s.display=3D"" else s.display=3D"none" } function _BSetA_Border(){ b =3D gBtnArr[this.mObjId]; if( b !=3D null ) b.S= etActive() } function _BSetI_Border(){ b =3D gBtnArr[this.mObjId]; if( b !=3D null ) b.S= etInactive() } var gHiliteClr=3D"THREEDHIGHLIGHT",gShadowClr=3D"THREEDSHADOW",gFaceClr=3D"= THREEDFACE" var gBtnArr =3D new Array() gBtnArr["nb_otl"] =3D new TxtBtn( "nb_otl","nb_otlElem",parent.ToggleOtlPan= e,GetOtlState ) gBtnArr["nb_otlElem"] =3D new TxtBtn( "nb_otl","nb_otlElem",parent.ToggleOt= lPane,GetOtlState ) gBtnArr["nb_nts"] =3D new TxtBtn( "nb_nts","nb_ntsElem",parent.ToggleNtsPan= e,GetNtsState ) gBtnArr["nb_prev"]=3D new ImgBtn( "nb_prev","nb_prevBorder",30,parent.GoToP= revSld ) gBtnArr["nb_next"]=3D new ImgBtn( "nb_next","nb_nextBorder",30,parent.GoToN= extSld ) gBtnArr["nb_sldshw"]=3D new ImgBtn( "nb_sldshw","nb_sldshwBorder",18,parent= .FullScreen ) gBtnArr["nb_sldshwBorder"] =3D new TxtBtn( "nb_sldshw","nb_sldshwBorder",pa= rent.FullScreen,null ) gBtnArr["nb_sldshwBorder"].SetActive =3D _BSetA_Border; gBtnArr["nb_sldshwBorder"].SetInactive =3D _BSetI_Border; gBtnArr["nb_sldshwText"] =3D new TxtBtn( "nb_sldshw","nb_sldshwText",parent= .FullScreen,null ) gBtnArr["nb_sldshwText"].SetActive =3D _BSetA_Border; gBtnArr["nb_sldshwText"].SetInactive =3D _BSetI_Border; gBtnArr["nb_voice"] =3D new ImgBtn( "nb_voice","nb_voiceBorder",18,parent.= ToggleVNarration ) gBtnArr["nb_otlTxt"]=3D new ImgBtn( "nb_otlTxt","nb_otlTxtBorder",23,parent= .ToggleOtlText ) gBtnArr["nb_nts"].m_flagId=3D "notes_flag" gBtnArr["nb_nts"].SetFlag =3D NtsBtnSetFlag gBtnArr["nb_otlTxt"].ChangeIcon=3D GetOtlTxtState var sNext=3D"Next",sPrev=3D"Previous",sEnd=3D"End Show",sFont=3D"Arial",sAr= row=3D"Arrow",sFreeform=3D"Freeform",sRect=3D"Rectangle",sOval=3D"Oval" function ShowMenu() { BuildMenu(); var doc=3DPPTSld.document.body,x=3DPPTSld.event.clientX+doc.scrollLeft,y= =3DPPTSld.event.clientY+doc.scrollTop m =3D PPTSld.document.all.item("ctxtmenu") m.style.pixelLeft=3Dx if( (x+m.scrollWidth > doc.clientWidth)&&(x-m.scrollWidth > 0) ) m.style.pixelLeft=3Dx-m.scrollWidth m.style.pixelTop=3Dy if( (y+m.scrollHeight > doc.clientHeight)&&(y-m.scrollHeight > 0) ) m.style.pixelTop=3Dy-m.scrollHeight m.style.display=3D"" } function _CM() { if( !parent.IsFullScrMode() ) return; if(!PPTSld.event.ctrlKey) { ShowMenu() return false } else HideMenu() } function BuildMenu() { if( PPTSld.document.all.item("ctxtmenu") ) return var mObj=3DCreateItem( PPTSld.document.body ) mObj.id=3D"ctxtmenu" mObj.style.visibility=3D"hidden" var s=3DmObj.style s.position=3D"absolute" s.cursor=3D"default" s.width=3D"120px" SetCMBorder(mObj,"menu","black") var iObj=3DCreateItem( mObj ) SetCMBorder( iObj, "threedhighlight","threedshadow" ) iObj.style.padding=3D2 CreateMenuItem( iObj,sNext,M_GoNextSld,M_True ) CreateMenuItem( iObj,sPrev,M_GoPrevSld,M_HasPrevSld ) CreateSeparator( iObj ) CreateMenuItem( iObj,sEnd,M_End,M_True ) mObj.style.visibility=3D"visible" } function Cancel() { window.event.cancelBubble=3Dtrue; window.event.returnVa= lue=3Dfalse } function Highlight() { ChangeClr("activecaption","threedhighlight") } function Deselect() { ChangeClr("threedface","menutext") } function Perform() { e=3DPPTSld.event.srcElement if( e.type=3D=3D"menuitem" && e.IsActive() ) e.Action() else PPTSld.event.cancelBubble=3Dtrue } function ChangeClr( bg,clr ) { e=3DPPTSld.event.srcElement if( e.type=3D=3D"menuitem" && e.IsActive() ) { e.style.backgroundColor=3Dbg e.style.color=3Dclr } } function M_HasPrevSld() { return( parent.HasPrevSld() ) } function M_GoNextSld() { if( gIsEndShow ) M_End(); 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Outline
= = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
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