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### 龙星计划课程:信息检索Statistical Language Models for IR

ChengXiang Zhai (翟成祥)

Department of Computer Science

Graduate School of Library & Information Science

Institute for Genomic Biology, Statistics

University of Illinois, Urbana-Champaign

http://www-faculty.cs.uiuc.edu/~czhai, [email protected]

• More about statistical language models in general

• Systematic review of language models for IR

• The basic language modeling approach

• KL-divergence retrieval model and feedback

• Language models for special retrieval tasks

### More about statistical language models in general

• A probability distribution over word sequences

• p(“Today is Wednesday”)  0.001

• p(“Today Wednesday is”)  0.0000000000001

• p(“The eigenvalue is positive”)  0.00001

• Context/topic dependent!

• Can also be regarded as a probabilistic mechanism for “generating” text, thus also called a “generative” model

• Provides a principled way to quantify the uncertainties associated with natural language

• Allows us to answer questions like:

• Given that we see “John” and “feels”, how likely will we see “happy” as opposed to “habit” as the next word? (speech recognition)

• Given that we observe “baseball” three times and “game” once in a news article, how likely is it about “sports”? (text categorization, information retrieval)

• Given that a user is interested in sports news, how likely would the user use “baseball” in a query? (information retrieval)

Source-Channel Framework(Model of Communication System [Shannon 48] )

Transmitter

(encoder)

Noisy

Channel

(decoder)

Source

Destination

X

Y

X’

P(X)

P(X|Y)=?

P(Y|X)

(Bayes Rule)

When X is text, p(X) is a language model

Many Examples:

Speech recognition: X=Word sequence Y=Speech signal

Machine translation: X=English sentence Y=Chinese sentence

OCR Error Correction: X=Correct word Y= Erroneous word

Information Retrieval: X=Document Y=Query

Summarization: X=Summary Y=Document

• Define the probabilistic model

• Event, Random Variables, Joint/Conditional Prob’s

• P(w1 w2 ... wn)=f(1, 2 ,…,m)

• Estimate model parameters

• Tune the model to best fit the data and our prior knowledge

• i=?

• Apply the model to a particular task

• Many applications

The Simplest Language Model(Unigram Model)

• Generate a piece of text by generating each word independently

• Thus, p(w1 w2 ... wn)=p(w1)p(w2)…p(wn)

• Parameters: {p(wi)} p(w1)+…+p(wN)=1 (N is voc. size)

• Essentially a multinomial distribution over words

• A piece of text can be regarded as a sample drawn according to this word distribution

Text mining

paper

Food nutrition

paper

(Unigram) Language Model 

p(w| )

Sampling

Document d

text 0.2

mining 0.1

assocation 0.01

clustering 0.02

food 0.00001

Topic 1:

Text mining

Given , p(d| ) varies according to d

food 0.25

nutrition 0.1

healthy 0.05

diet 0.02

Topic 2:

Health

text ?

mining ?

assocation ?

database ?

query ?

10/100

5/100

3/100

3/100

1/100

How good is the estimated model ?

It gives our document sample the highest prob,

(Unigram) Language Model 

p(w| )=?

Estimation

Document

text 10

mining 5

association 3

database 3

algorithm 2

query 1

efficient 1

Total #words

=100

• There are stable language-independent patterns in how people use natural languages

• A few words occur very frequently; most occur rarely. E.g., in news articles,

• Top 4 words: 10~15% word occurrences

• Top 50 words: 35~40% word occurrences

• The most frequent word in one corpus may be rare in another

Word

Freq.

Most useful words (Luhn 57)

Is “too rare” a problem?

Biggest

data structure

(stop words)

Word Rank (by Freq)

Generalized Zipf’s law:

Applicable in many domains

• rank * frequency  constant

• N-gram language models

• In general, p(w1 w2 ... wn)=p(w1)p(w2|w1)…p(wn|w1 …wn-1)

• n-gram: conditioned only on the past n-1 words

• E.g., bigram: p(w1 ... wn)=p(w1)p(w2|w1) p(w3|w2) …p(wn|wn-1)

• Remote-dependence language models (e.g., Maximum Entropy model)

• Structured language models (e.g., probabilistic context-free grammar)

• Will not be covered in detail in this course. If interested, read [Jelinek 98, Manning & Schutze 99, Rosenfeld 00]

• Difficulty in moving toward more complex models

• They involve more parameters, so need more data to estimate (A doc is an extremely small sample)

• They increase the computational complexity significantly, both in time and space

• Capturing word order or structure may not add so much value for “topical inference”

• But, using more sophisticated models can still be expected to improve performance ...

• Direct evaluation criterion: How well does the model fit the data to be modeled?

• Example measures: Data likelihood, perplexity, cross entropy, Kullback-Leibler divergence (mostly equivalent)

• Indirect evaluation criterion: Does the model help improve the performance of the task?

• Specific measure is task dependent

• For retrieval, we look at whether a model helps improve retrieval accuracy

• We hope more “reasonable” LMs would achieve better retrieval performance

• How the source-channel framework can model many different problems

• Why unigram LMs seem to be sufficient for IR

• Zipf’s law

### Systematic Review of Language Models for IR

Representative LMs for IR (up to 2006)

1998

1999

2000

2001

2002

2003

2004

2005 -

Query likelihood scoring

Ponte & Croft 98

Hiemstra & Kraaij 99;

Miller et al. 99

Smoothing examined

Zhai & Lafferty 01a

Bayesian Query likelihood

Zaragoza et al. 03.

Theoretical justification

Lafferty & Zhai 01a,01b

URL prior

Kraaij et al. 02

Parameter

sensitivity

Ng 00

Time prior

Li & Croft 03

Two-stage LMs

Zhai & Lafferty 02

Basic LM (Query Likelihood)

Beyond unigram

Song & Croft 99

Term-specific smoothing

Hiemstra 02

Cluster LM

Kurland & Lee 04

Cluster smoothing

Liu & Croft 04; Tao et al. 06

Improved

Basic LM

Title LM

Jin et al. 02

Concept Likelihood

Srikanth & Srihari 03

Thesauri

Cao et al. 05

Translation model

Berger & Lafferty 99

Dependency LM

Gao et al. 04

Relevance LM

Lavrenko & Croft 01

Parsimonious LM

Hiemstra et al. 04

Pesudo Query

Kurland et al. 05

Query/Rel

Model &

Feedback

Rel. Query FB

Nallanati et al 03

Model-based FB

Zhai & Lafferty 01b

Query expansion

Bai et al. 05

Markov-chain query model

Lafferty & Zhai 01b

Rebust Est.

Tao & Zhai 06

Special

Lavrenko et al. 02

Shen et al. 05

Xu & Croft 99

Ogilvie & Callan 03

Zhai et al. 03

Xu et al. 01

Zhang et al. 02

Tan et al. 06

Cronen-Townsend et al. 02

Si et al. 02

Kurland & Lee 05

Dissertations

Ponte 98

Hiemstra 01

Berger 01

Lavrenko 04

Kraaij 04

Zhai 02

Tao 06

Kurland 06

Srikanth 04

Ponte & Croft’s Pioneering Work [Ponte & Croft 98]

• Contribution 1:

• A new “query likelihood” scoring method: p(Q|D)

• [Maron and Kuhns 60] had the idea of query likelihood, but didn’t work out how to estimate p(Q|D)

• Contribution 2:

• Connecting LMs with text representation and weighting in IR

• [Wong & Yao 89] had the idea of representing text with a multinomial distribution (relative frequency), but didn’t study the estimation problem

• Good performance is reported using the simple query likelihood method

• At about the same time as SIGIR 98, in TREC 7, two groups explored similar ideas independently: BBN [Miller et al., 99] & Univ. of Twente [Hiemstra & Kraaij 99]

• In TREC-8, Ng from MIT motivated the same query likelihood method in a different way [Ng 99]

• All following the simple query likelihood method; methods differ in the way the model is estimated and the event model for the query

• All show promising empirical results

• Main problems:

• Feedback is explored heuristically

• Lack of understanding why the method works….

• Attempt to understand why LMs work [Zhai & Lafferty 01a, Lafferty & Zhai 01a, Ponte 01, Greiff & Morgan 03, Sparck Jones et al. 03, Lavrenko 04]

• Further extend/improve the basic LMs [Song & Croft 99, Berger & Lafferty 99, Jin et al. 02, Nallapati & Allan 02, Hiemstra 02, Zaragoza et al. 03, Srikanth & Srihari 03, Nallapati et al 03, Li &Croft 03, Gao et al. 04, Liu & Croft 04, Kurland & Lee 04,Hiemstra et al. 04,Cao et al. 05, Tao et al. 06]

• Explore alternative ways of using LMs for retrieval (mostly query/relevance model estimation) [Xu & Croft 99, Lavrenko & Croft 01, Lafferty & Zhai 01a, Zhai & Lafferty 01b, Lavrenko 04, Kurland et al. 05, Bai et al. 05,Tao & Zhai 06]

• Explore the use of SLMs for special retrieval tasks [Xu & Croft 99, Xu et al. 01, Lavrenko et al. 02, Cronen-Townsend et al. 02, Zhang et al. 02, Ogilvie & Callan 03, Zhai et al. 03, Kurland & Lee 05, Shen et al. 05, Balog et al. 06, Fang & Zhai 07]

### Review of LM for IR: Part 1. Basic Language Modeling Approach

The Basic LM Approach[Ponte & Croft 98]

Language Model

text ?

mining ?

assocation ?

clustering ?

food ?

?

Which model would most

likely have generated

this query?

food ?

nutrition ?

healthy ?

diet ?

Document

Query =

“data mining algorithms”

Text mining

paper

Food nutrition

paper

Doc LM

Query likelihood

d1

p(q| d1)

p(q| d2)

d2

p(q| dN)

dN

d1

q

d2

dN

• Multi-Bernoulli: Modeling word presence/absence

• q= (x1, …, x|V|), xi =1 for presence of word wi; xi =0 for absence

• Parameters: {p(wi=1|d), p(wi=0|d)} p(wi=1|d)+ p(wi=0|d)=1

• Multinomial (Unigram LM): Modeling word frequency

• q=q1,…qm , where qj is a query word

• c(wi,q) is the count of word wi in query q

• Parameters: {p(wi|d)} p(w1|d)+… p(w|v||d) = 1

[Ponte & Croft 98]uses Multi-Bernoulli; most other work uses multinomial

Multinomial seems to work better[Song & Croft 99, McCallum & Nigam 98,Lavrenko 04]

• Document ranking based on query likelihood

Document language model

• Retrieval problem  Estimation of p(wi|d)

• Smoothing is an important issue, and distinguishes different approaches

• Many smoothing methods are available

### Which smoothing method is the best?

It depends on the data and the task!

Cross validation is generally used to choose the best method and/or set the smoothing parameters…

For retrieval, Dirichlet prior performs well…

Backoff smoothing [Katz 87] doesn’t work well due to a lack of 2nd-stage smoothing…

Note that many other smoothing methods exist

See [Chen & Goodman 98] and other publications in speech recognition…

Comparison of Three Methods[Zhai & Lafferty 01a]

Comparison is performed on a variety of test collections

The Dual-Role of Smoothing [Zhai & Lafferty 02]

Keyword

queries

Verbose

queries

long

long

short

short

Why does query type affect smoothing sensitivity?

Intuitively, d2 should have a higher score,

but p(q|d1)>p(q|d2)…

Query = “the algorithms for data mining”

P(w|REF) 0.2 0.00001 0.2 0.00001 0.00001

Smoothed p(w|d1): 0.184 0.000109 0.182 0.000209 0.000309

Smoothed p(w|d2):0.182 0.000109 0.181 0.000309 0.000409

Content words

Query = “the algorithms for data mining”

pDML(w|d1):0.04 0.001 0.02 0.002 0.003

pDML(w|d2): 0.02 0.001 0.01 0.003 0.004

p( “algorithms”|d1) = p(“algorithm”|d2)

p( “data”|d1) < p(“data”|d2)

p( “mining”|d1) < p(“mining”|d2)

So we should make p(“the”) and p(“for”) less different for all docs,

and smoothing helps achieve this goal…

Two-stage Smoothing [Zhai & Lafferty 02]

Stage-1

-Explain unseen words

-Dirichlet prior(Bayesian)

Stage-2

-Explain noise in query

-2-component mixture

c(w,d)

+p(w|C)

(1-)

+ p(w|U)

|d|

+

Collection LM

P(w|d) =

User background model

Can be approximated by p(w|C)

Estimating  using leave-one-out [Zhai & Lafferty 02]

w1

Leave-one-out

P(w1|d- w1)

log-likelihood

w2

P(w2|d- w2)

Maximum Likelihood Estimator

...

wn

Newton’s Method

P(wn|d- wn)

Now, suppose we leave “e” out…

 doesn’t have to be big

 must be big! more smoothing

20 word by author1

Suppose we keep sampling and get 10 more words. Which author is likely to

“write” more new words?

abc abc ab c d d

abc cd d d

abd ab ab ab ab

cd d e cd e

20 word by author2

abc abc ab c d d

abe cb e f

acf fb ef aff abef

cdc db ge f s

The amount of smoothing is closely related to

the underlying vocabulary size

Estimating  using Mixture Model[Zhai & Lafferty 02]

Stage-2

Stage-1

1

d1

P(w|d1)

(1-)p(w|d1)+p(w|U)

...

Query

Q=q1…qm

… ...

N

dN

P(w|dN)

(1-)p(w|dN)+p(w|U)

Estimated in stage-1

Maximum Likelihood Estimator

Expectation-Maximization (EM) algorithm

Automatic 2-stage results  Optimal 1-stage results [Zhai & Lafferty 02]

Average precision (3 DB’s + 4 query types, 150 topics)

* Indicates significant difference

Completely automatic tuning of parameters IS POSSIBLE!

• Different smoothing strategies

• Hidden Markov Models (essentially linear interpolation) [Miller et al. 99]

• Smoothing with an IDF-like reference model [Hiemstra & Kraaij 99]

• Performance tends to be similar to the basic LM approach

• Many other possibilities for smoothing [Chen & Goodman 98]

• Different priors

• Link information as prior leads to significant improvement of Web entry page retrieval performance [Kraaij et al. 02]

• Time as prior [Li & Croft 03]

• PageRank as prior [Kurland & Lee 05]

• Passage retrieval [Liu & Croft 02]

### Review of LM for IR: Part 2. Advanced Language Modeling Approaches

• Capturing limited dependencies

• Bigrams/Trigrams [Song & Croft 99]; Grammatical dependency [Nallapati & Allan 02, Srikanth & Srihari 03, Gao et al. 04]

• Generally insignificant improvement as compared with other extensions such as feedback

• Full Bayesian query likelihood [Zaragoza et al. 03]

• Performance similar to the basic LM approach

• Translation model for p(Q|D,R) [Berger & Lafferty 99, Jin et al. 02,Cao et al. 05]

• Address polesemy and synonyms; improves over the basic LM methods, but computationally expensive

• Cluster-based smoothing/scoring [Liu & Croft 04, Kurland & Lee 04,Tao et al. 06]

• Improves over the basic LM, but computationally expensive

• Parsimonious LMs [Hiemstra et al. 04]:

• Using a mixture model to “factor out” non-discriminative words

• Directly modeling the “translation” relationship between words in the query and words in a doc

• When relevance judgments are available, (q,d) serves as data to train the translation model

• Without relevance judgments, we can use synthetic data [Berger & Lafferty 99],<title, body>[Jin et al. 02] , or thesauri [Cao et al. 05]

Basic translation model

Translation model

Regular doc LM

• Cluster-based smoothing: Smooth a document LM with a cluster of similar documents [Liu & Croft 04]: improves over the basic LM, but insignificantly

• Document expansion smoothing: Smooth a document LM with the neighboring documents (essentially one cluster per document) [Tao et al. 06] : improves over the basic LM more significantly

• Cluster-based query likelihood: Similar to the translation model, but “translate” the whole document to the query through a set of clusters [Kurland & Lee 04]

How likely doc D

belongs to cluster C

Likelihood of Q

given C

Only effective when interpolated with the basic LM scores

Rel. doc model

NonRel. doc model

“Rel. query” model

(q1,d1,1)

(q1,d2,1)

(q1,d3,1)

P(D|Q,R=1)

(q1,d4,0)

(q1,d5,0)

(q3,d1,1)

P(D|Q,R=0)

Parameter

Estimation

(q4,d1,1)

(q5,d1,1)

(q6,d2,1)

(q6,d3,0)

P(Q|D,R=1)

Query-based feedback

Doc-based feedback

Classic Prob. Model

Query likelihood

(“Language Model”)

Initial retrieval:

- query as rel doc vs. doc as rel query

- P(Q|D,R=1) is more accurate

Feedback:

- P(D|Q,R=1) can be improved for the currentquery and futuredoc

- P(Q|D,R=1) can also be improved, but for current doc and futurequery

• Feedback as machine learning: many possibilities

• Standard ML: Given examples of relevant (and non-relevant) documents, learn how to classify a new document as either “relevant” or “non-relevant”.

• “Modified” ML: Given a query and examples of relevant (and non-relevant) documents, learn how to rank new documents based on relevance

• Challenges:

• Sparse data

• Censored sample

• How to deal with query?

• Modeling noise in pseudo feedback (as semi-supervised learning)

• Feedback as query expansion: traditional IR

• Step 1: Term selection

• Step 2: Query expansion

• Step 3: Query term re-weighting

• Traditional IR is still robust (Rocchio), but ML approaches can potentially be more accurate

• Traditional query expansion [Ponte 98, Miller et al. 99, Ng 99]

• Improvement is reported, but there is a conceptual inconsistency

• What’s an expanded query, a piece of text or a set of terms?

• Avoid expansion

• Query term reweighting [Hiemstra 01, Hiemstra 02]

• Translation models [Berger & Lafferty 99, Jin et al. 02]

• Only achieving limited feedback

• Doing relevant query expansion instead [Nallapati et al 03]

• The difficulty is due to the lack of a query/relevance model

• The difficulty can be overcome with alternative ways of using LMs for retrieval (e.g., relevance model [Lavrenko & Croft 01] , Query model estimation [Lafferty & Zhai 01b; Zhai & Lafferty 01b])

• Classic Probabilistic Model :Doc-Generation as opposed to Query-generation

• Natural for relevance feedback

• Challenge: Estimate p(D|Q,R=1) without relevance feedback; relevance model [Lavrenko & Croft 01] provides a good solution

• Probabilistic Distance Model :Similar to the vector-space model, but with LMs as opposed to TF-IDF weight vectors

• A popular distance function: Kullback-Leibler (KL) divergence, covering query likelihood as a special case

• Retrieval is now to estimate query & doc models and feedback is treated as query LM updating [Lafferty & Zhai 01b; Zhai & Lafferty 01b]

Both methods outperform the basic LM significantly

Relevance Model Estimation[Lavrenko & Croft 01]

• Question: How to estimate P(D|Q,R) (or p(w|Q,R)) without relevant documents?

• Key idea:

• Treat query as observations about p(w|Q,R)

• Approximate the model space with document models

• Two methods for decomposing p(w,Q)

• Independent sampling (Bayesian model averaging)

• Conditional sampling: p(w,Q)=p(w)p(Q|w)

Original formula in [Lavranko &Croft 01]

Query Model Estimation[Lafferty & Zhai 01b, Zhai & Lafferty 01b]

• Question: How to estimate a better query model than the ML estimate based on the original query?

• “Massive feedback”: Improve a query model through co-occurrence pattern learned from

• A document-term Markov chain that outputs the query [Lafferty & Zhai 01b]

• Thesauri, corpus [Bai et al. 05,Collins-Thompson & Callan 05]

• Model-based feedback: Improve the estimate of query model by exploiting pseudo-relevance feedback

• Update the query model by interpolating the original query model with a learned feedback model [ Zhai & Lafferty 01b]

• Estimate a more integrated mixture model using pseudo-feedback documents [ Tao & Zhai 06]

### Review of LM for IR: Part 3. KL-divergence retrieval model and feedback

• Unigram similarity model

• Retrieval  Estimation of Q and D

• Special case: = empirical distribution of q recovers “query-likelihood”

query entropy

(ignored for ranking)

Feedback as Model Interpolation(Rocchio for Language Models)

=0

=1

No feedback

Full feedback

Document D

Results

Query Q

Feedback Docs

F={d1, d2 , …, dn}

Generative model

Generative Mixture Model

Background words

w

P(w| C)

F={d1,…,dn}

P(source)

Topic words

w

1-

P(w|  )

Maximum Likelihood

 = Noise in feedback documents

ML

Estimator

the 0.2

a 0.1

we 0.01

to 0.02

text 0.0001

mining 0.00005

Observed

Doc(s)

Known

Background

p(w|C)

=0.7

Unknown

query topic

p(w|F)=?

“Text mining”

text =?

mining =?

association =?

word =?

=0.3

Suppose,

we know

the identity of each word ...

E-step

M-step

Identity (“hidden”) variable: zi{1 (background), 0(topic)}

zi

1

1

1

1

0

0

0

1

0

...

Suppose the parameters are all known, what’s a reasonable guess of zi?

- depends on  (why?)

- depends on p(w|C) and p(w|F) (how?)

the

paper

presents

a

text

mining

algorithm

the

paper

...

Initially, set p(w| F) to some random value, then iterate …

Expectation-Step:

Augmenting data by guessing hidden variables

Maximization-Step

With the “augmented data”, estimate parameters

using maximum likelihood

Assume =0.5

Trec topic 412: “airport security”

Mixture model approach

Web database

Top 10 docs

=0.9

=0.7

Model-based feedback Improves over Simple LM [Zhai & Lafferty 01b]

Translation models, Relevance models, and Feedback-based query models have all been shown to improve performance significantly over the simple LMs (Parameter tuning is necessary in many cases, but see [Tao & Zhai 06] for “parameter-free” pseudo feedback)

• The KL-divergence retrieval formula as a generalization of the query likelihood method

• How the mixture model for feedback works

• Know how to estimate the simple mixture model using EM

### Review of LM for IR: Part 4. Language models for special retrieval tasks

Translation model

Estimate with a

bilingual lexicon

Or

Parallel corpora

Estimate with parallel corpora

• Use query in language A (e.g., English) to retrieve documents in language B (e.g., Chinese)

• Cross-lingual p(Q|D,R) [Xu et al 01]

• Cross-lingual p(D|Q,R) [Lavrenko et al 02]

English

Chinese word

English

Chinese

Method 1:

Method 2:

• Retrieve documents from multiple collections

• The task is generally decomposed into two subtasks: Collection selection and result fusion

• Using LMs for collection selection [Xu & Croft 99, Si et al. 02]

• Treat collection selection as “retrieving collections” as opposed to “documents”

• Estimate each collection model by maximum likelihood estimate [Si et al. 02] or clustering [Xu & Croft 99]

• Using LMs for result fusion [ Si et al. 02]

• Assume query likelihood scoring for all collections, but on each collection, a distinct reference LM is used for smoothing

• Adjust the bias score p(Q|D,Collection) to recover the fair score p(Q|D)

Structured Document Retrieval[Ogilvie & Callan 03]

• Want to combine different parts of a

• document with appropriate weights

• Anchor text can be treated as a “part” of a

• document

• - Applicable to XML retrieval

D

Title

Abstract

Body-Part1

Body-Part2

D1

D2

Select Dj and generate a query word using Dj

D3

Dk

“part selection” prob. Serves as weight for Dj

Can be trained using EM

Personalized/Context-Sensitive Search[Shen et al. 05, Tan et al. 06]

• User information and search context can be used to estimate a better query model

Context-independent Query LM:

Context-sensitive Query LM:

Refinement of this model leads to specific retrieval formulas

Simple models often end up interpolating many unigram language models based on different sources of evidence, e.g., short-term search history [Shen et al. 05] or long-term search history [Tan et al. 06]

• Given two documents D1 and D2, decide how redundant D1 (or D2) is w.r.t. D2 (or D1)

• Redundancy of D1  “to what extent can D1 be explained by a model estimated based on D2”

• Use a unigram mixture model [Zhai 02]

• See [Zhang et al. 02] for a 3-component redundancy model

• Along a similar line, we could measure document similarity in an asymmetric way [Kurland & Lee 05]

LM for D2

Reference LM

Maximum Likelihood estimator

EM algorithm

Measure of redundancy

Predicting Query Difficulty [Cronen-Townsend et al. 02]

• Observations:

• Discriminative queries tend to be easier

• Comparison of the query model and the collection model can indicate how discriminative a query is

• Method:

• Define “query clarity” as the KL-divergence between an estimated query model or relevance model and the collection LM

• An enriched query LM can be estimated by exploiting pseudo feedback (e.g., relevance model)

• Correlation between the clarity scores and retrieval performance is found

Expert Finding [Balog et al. 06, Fang & Zhai 07]

• Task: Given a topic T, a list of candidates {Ci} , and a collection of support documents S={Di}, rank the candidates according to the likelihood that a candidate C is an expert on T.

• Retrieval analogy:

• Query = topic T

• Document = Candidate C

• Rank according to P(R=1|T,C)

• Similar derivations to those on slides 55-56, 64 can be made

• Candidate generation model:

• Topic generation model:

### Summary

• Pros:

• Statistical foundations (better parameter setting)

• More principled way of handling term weighting

• More powerful for modeling subtopics, passages,..

• Leverage LMs developed in related areas

• Empirically as effective as well-tuned traditional models with potential for automatic parameter tuning

• Cons:

• Lack of discrimination (a common problem with generative models)

• Less robust in some cases (e.g., when queries are semi-structured)

• Computationally complex

• Empirically, performance appears to be inferior to well-tuned full-fledged traditional methods (at least, no evidence for beating them)

Framework and justification for using LMs for IR

Several effective models are developed

Basic LM with Dirichlet prior smoothing is a reasonable baseline

Basic LM with informative priors often improves performance

Translation model handles polysemy & synonyms

Relevance model incorporates LMs into the classic probabilistic IR model

KL-divergence model ties feedback with query model estimation

Mixture models can model redundancy and subtopics

Completely automatic tuning of parameters is possible

LMs can be applied to virtually any retrieval task with great potential for modeling complex IR problems

• Challenge 1: Establish a robust and effective LM that

• Optimizes retrieval parameters automatically

• Performs as well as or better than well-tuned traditional retrieval methods with pseudo feedback

• Is as efficient as traditional retrieval methods

• Challenge 2: Demonstrate consistent and substantial improvement by going beyond unigram LMs

• Model limited dependency between terms

• Derive more principled weighting methods for phrases

Can LMs consistently (convincingly) outperform traditional methods

without sacrificing efficiency?

Can we do much better by going beyond unigram LMs?

• Challenge 3: Develop LMs that can support “life-time learning”

• Develop LMs that can improve accuracy for a current query through learning from past relevance judgments

• Support collaborative information retrieval

• Challenge 4: Develop LMs that can model document structures and subtopics

• Recognize query-specific boundaries of relevant passages

• Passage-based/subtopic-based feedback

• Combine different structural components of a document

How can we learn effectively from past relevance judgments?

How can we break the document unit in a principled way?

• Challenge 5: Develop LMs to support personalized search

• Infer and track a user’s interests with LMs

• Incorporate user’s preferences and search context in retrieval

• Customize/organize search results according to user’s interests

• Challenge 6: Generalize LMs to handle relational data

• Develop LMs for semi-structured data (e.g., XML)

• Develop LMs to handle structured queries

• Develop LMs for keyword search in relational databases

How can we exploit user information and search context to improve search?

What role can LMs play when combining text with relational data?

• Challenge 7: Develop LMs for hypertext retrieval

• Combine LMs with link information

• Modeling and exploiting anchor text

• Develop a unified LM for hypertext search

• Challenge 8: Develop LMs for retrieval with complex information needs, e.g.,

• Subtopic retrieval

• Entity retrieval (e.g. expert search)

How can we develop an effective unified retrieval model for Web search?

How can we exploit LMs to develop models for complex retrieval tasks?

• General picture of language models for IR

• The KL-divergence retrieval formula as a generalization of the query likelihood method

• How the mixture model for feedback works

• Know how to estimate the simple mixture model using EM

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