The Vector Space Model LBSC 796/CMSC828o Session 3, February 9, 2004 Douglas W. Oard.
Language Models LBSC 796/CMSC 828o Session 4, February 16, 2004 Douglas W. Oard.
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Transcript of Language Models LBSC 796/CMSC 828o Session 4, February 16, 2004 Douglas W. Oard.
Language Models
LBSC 796/CMSC 828o
Session 4, February 16, 2004
Douglas W. Oard
• Questions
• The meaning of “maybe”
• Probabilistic retrieval
• Comparison with vector space model
Agenda
Muddiest Points
• Why distinguish utility and relevance?• How coordination measure is ranked Boolean• Why use term weights?• The meaning of DF• The problem with log(1)=0• How the vectors are built• How to do cosine normalization (5)• Why to do cosine normalization (2)• Okapi graphs
Supporting the Search Process
SourceSelection
Search
Query
Selection
Ranked List
Examination
Document
Delivery
Document
QueryFormulation
IR System
Indexing Index
Acquisition Collection
Looking Ahead
• We ask “is this document relevant?”– Vector space: we answer “somewhat”– Probabilistic: we answer “probably”
• The key is to know what “probably” means– First, we’ll formalize that notion– Then we’ll apply it to retrieval
Probability
• What is probability?– Statistical: relative frequency as n – Subjective: degree of belief
• Thinking statistically– Imagine a finite amount of “stuff”– Associate the number 1 with the total amount– Distribute that “mass” over the possible events
Statistical Independence
• A and B are independent if and only if: P(A and B) = P(A) P(B)
• Independence formalizes “unrelated”– P(“being brown eyed”) = 85/100– P(“being a doctor”) = 1/1000– P(“being a brown eyed doctor”) = 85/100,000
Dependent Events
• Suppose”– P(“having a B.S. degree”) = 2/10– P(“being a doctor”) = 1/1000
• Would you expect– P(“having a B.S. degree and being a doctor”)
= 2/10,000 ???
• Extreme example:– P(“being a doctor”) = 1/1000– P(“having studied anatomy”) = 12/1000
Conditional Probability• P(A | B) P(A and B) / P(B)
A
B
A and B
• P(A) = prob of A relative to the whole space
• P(A|B) = prob of A considering only the cases where B is known to be true
More on Conditional Probability
• Suppose– P(“having studied anatomy”) = 12/1000– P(“being a doctor and having studied anatomy”) = 1/1000
• Consider– P(“being a doctor” | “having studied anatomy”) = 1/12
• But if you assume all doctors have studied anatomy– P(“having studied anatomy” | “being a doctor”) = 1
Useful restatement of definition: P(A and B) = P(A|B) x P(B)
Some Notation
• Consider – A set of hypotheses: H1, H2, H3– Some observable evidence O
• P(O|H1) = probability of O being observed if we knew H1 were true
• P(O|H2) = probability of O being observed if we knew H2 were true
• P(O|H3) = probability of O being observed if we knew H3 were true
An Example
• Let– O = “Joe earns more than $80,000/year”
– H1 = “Joe is a doctor”
– H2 = “Joe is a college professor”
– H3 = “Joe works in food services”
• Suppose we do a survey and we find out– P(O|H1) = 0.6
– P(O|H2) = 0.07
– P(O|H3) = 0.001
• What should be our guess about Joe’s profession?
Bayes’ Rule
• What’s P(H1|O)? P(H2|O)? P(H3|O)?
• Theorem:
P(H | O) = P(O | H) x P(H)
P(O) Posteriorprobability
Priorprobability
• Notice that the prior is very important!
Back to the Example
• Suppose we also have good data about priors:– P(O|H1) = 0.6 P(H1) = 0.0001 doctor– P(O|H2) = 0.07 P(H2) = 0.001 prof– P(O|H3) = 0.001 P(H3) = 0.2 food
• We can calculate– P(H1|O) = 0.00006 / P(“earning >
$70K/year”)– P(H2|O) = 0.0007 / P(“earning > $70K/year”)– P(H3|O) = 0.0002 / P(“earning > $70K/year”)
Key Ideas
• Defining probability using frequency
• Statistical independence
• Conditional probability
• Bayes’ rule
• Questions
• Defining probability
• Using probability for retrieval– Language modeling– Inference networks
• Comparison with vector space model
Agenda
Probability Ranking Principle• Assume binary relevance/document independence
– Each document is either relevant or it is not– Relevance of one doc reveals nothing about another
• Assume the searcher works down a ranked list– Seeking some number of relevant documents
• Theorem (provable from assumptions):– Documents should be ranked in order of decreasing
probability of relevance to the query,
P(d relevant-to q)
Probabilistic Retrieval Strategy
• Estimate how terms contribute to relevance– How do TF, DF, and length influence your
judgments about document relevance? (e.g., Okapi)
• Combine to find document relevance probability
• Order documents by decreasing probability
Where do the probabilities fit?
Comparison Function
Representation Function
Query Formulation
Human Judgment
Representation Function
Retrieval Status Value
Utility
Query
Information Need Document
Query Representation Document Representation
Que
ry P
roce
ssin
g
Doc
umen
t P
roce
ssin
g
P(d is Rel | q)
Binary Independence Model
• Binary refers again to binary relevance
• Assume “term independence”– Presence of one term tells nothing about another
• Assume “uniform priors”– P(d) is the same for all d
“Okapi” Term Weights
5.0
5.0log*
5.05.1 ,
,,
j
j
jii
jiji DF
DFN
TFLL
TFw
0.0
0.2
0.4
0.6
0.8
1.0
0 5 10 15 20 25
Raw TF
Oka
pi
TF 0.5
1.0
2.0
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
0 5 10 15 20 25
Raw DF
IDF Classic
Okapi
LL /
TF component IDF component
Stochastic Language Models
• Models probability of generating any string
0.2 the
0.1 a
0.01 man
0.01 woman
0.03 said
0.02 likes
…
the man likes the woman
0.2 0.01 0.02 0.2 0.01
multiply
Model M
P(s | M)
Language Models, cont’d
• Models probability of generating any string
0.2 the
0.1 a
0.01 man
0.01 woman
0.03 said
0.02 likes
…
Model M1
0.2 the
0.1 yon
0.001 class
0.01 maiden
0.03 sayst
0.02 pleaseth
…
Model M2
maidenclass pleaseth yonthe
0.00050.01 0.0001 0.00010.2
0.010.0001 0.02 0.10.2
P(s|M2) > P(s|M1)
Retrieval with Language Models
• Treat each document as the basis for a model
• Rank document d based on P(d | q)– P(d | q) = P(q | d) x P(d) / P(q)
• P(q) is same for all documents, can’t change ranks
• P(d) [the prior] is often treated as the same for all d– But we could use criteria like authority, length, genre
• P(q | d) is the probability of q given d’s model
– Same as ranking by P(q | d)
Computing P(q | d)
• Build a smoothed language model for d– Count the frequency of each term in d– Count the frequency of each term in the collection– Combine the two in some way– Redistribute probabilities to unobserved events
• Example: add 1 to every count
• Combine the probability for the full query– Summing over the terms in q is a soft “OR”
Key Ideas
• Probabilistic methods formalize assumptions– Binary relevance– Document independence– Term independence– Uniform priors– Top-down scan
• Natural framework for combining evidence– e.g., non-uniform priors
Inference Networks
• A flexible way of combining term weights– Boolean model– Binary independence model– Probabilistic models with weaker assumptions
• Key concept: rank based on P(d | q)– P(d | q) = P(q | d) x P(d) / P(q)
• Efficient large-scale implementation– InQuery text retrieval system from U Mass
A Boolean Inference Net
bat
d1 d2 d3 d4
cat fat hat mat pat rat vat
ANDOR
sat
AND I Information need
A Binary Independence Network
bat
d1 d2 d3 d4
cat fat hat mat pat rat vatsat
query
Probability Computation
• Turn on exactly one document at a time– Boolean: Every connected term turns on– Binary Ind: Connected terms gain their weight
• Compute the query value– Boolean: AND and OR nodes use truth tables– Binary Ind: Fraction of the possible weight w
w
t dt d
t dt
,
,
A Critique
• Most of the assumptions are not satisfied!– Searchers want utility, not relevance– Relevance is not binary– Terms are clearly not independent– Documents are often not independent
• The best known term weights are quite ad hoc– Unless some relevant documents are known
But It Works!
• Ranked retrieval paradigm is powerful– Well suited to human search strategies
• Probability theory has explanatory power– At least we know where the weak spots are– Probabilities are good for combining evidence
• Good implementations exist (InQuery, Lemur)– Effective, efficient, and large-scale
Comparison With Vector Space
• Similar in some ways– Term weights can be based on frequency– Terms often used as if they were independent
• Different in others– Based on probability rather than similarity– Intuitions are probabilistic rather than geometric
One Minute Paper
• Which assumption underlying the probabilistic retrieval model causes you the most concern, and why?