Loading in 5 sec....

龙星计划课程 : 信息检索 Statistical Language Models for IRPowerPoint Presentation

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

- 112 Views
- Uploaded on

Download Presentation
## PowerPoint Slideshow about ' 龙星计划课程 : 信息检索 Statistical Language Models for IR' - matteo

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

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

### Systematic Review of Language Models for IR

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

### Which smoothing method is the best?

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

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

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

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]

Outline

- More about statistical language models in general
- Systematic review of language models for IR
- The basic language modeling approach
- Advanced language models
- KL-divergence retrieval model and feedback
- Language models for special retrieval tasks

What is a Statistical LM?

- 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

Why is a LM Useful?

- 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

Receiver

(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

Basic Issues

- 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 Generation with Unigram LM

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

Estimation of Unigram LM

…

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,

but it doesn’t generalize well… More about this later…

(Unigram) Language Model

p(w| )=?

Estimation

Document

text 10

mining 5

association 3

database 3

algorithm 2

…

query 1

efficient 1

Total #words

=100

Empirical distribution of words

- 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

Zipf’s Law

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

More Sophisticated LMs

- 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]

Why Just Unigram Models?

- 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 ...

Evaluation of SLMs

- 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

What You Should Know

- How the source-channel framework can model many different problems
- Why unigram LMs seem to be sufficient for IR
- Zipf’s law

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

IR tasks

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

Early Work (1998-1999)

- 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….

Later Work (1999-)

- 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]

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

Ranking Docs by Query Likelihood

Doc LM

Query likelihood

d1

p(q| d1)

p(q| d2)

d2

p(q| dN)

dN

d1

q

d2

dN

Modeling Queries: Different Assumptions

- 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]

Retrieval as LM Estimation

- 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

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?

Another Reason for Smoothing

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)

Why would “leave-one-out” work?

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!

Variants of the Basic LM Approach

- 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]

Improving the Basic LM Approach

- 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

Translation Models

- 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/Scoring

- 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

Feedback and Doc/Query Generation

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

Overview of Feedback Techniques

- 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

Difficulty in Feedback with Query Likelihood

- 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])

Two Alternative Ways of Using LMs

- 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]

Kullback-Leibler (KL) Divergence Retrieval Model

- 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

How to Estimate F?

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 ...

Can We Guess the Identity?

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 …

An Example of EM Computation

Expectation-Step:

Augmenting data by guessing hidden variables

Maximization-Step

With the “augmented data”, estimate parameters

using maximum likelihood

Assume =0.5

Example of Feedback Query Model

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)

What Should You Know

- 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

Cross-Lingual IR

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:

Distributed IR

- 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]

Modeling Redundancy

- 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:

SLMs vs. Traditional IR

- 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)

What We Have Achieved So Far

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

Challenges and Future Directions

- 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?

Challenges and Future Directions (cont.)

- 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?

Challenges and Future Directions (cont.)

- 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?

Challenges and Future Directions (cont.)

- 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
- Readability constrained 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?

What You Should Know

- 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

References

[Agichtein & Cucerzan 05] E. Agichtein and S. Cucerzan, Predicting accuracy of extracting information from unstructured text collections, Proceedings of ACM CIKM 2005. pages 413-420.

[Baeza-Yates & Ribeiro-Neto 99] R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley, 1999.

[Bai et al. 05] Jing Bai, Dawei Song, Peter Bruza, Jian-Yun Nie, Guihong Cao, Query expansion using term relationships in language models for information retrieval, Proceedings of ACM CIKM 2005, pages 688-695.

[Balog et al. 06] K. Balog, L. Azzopardi, M. de Rijke, Formal models for expert finding in enterprise corpora, Proceedings of ACM SIGIR 2006, pages 43-50.

[Berger & Lafferty 99] A. Berger and J. Lafferty. Information retrieval as statistical translation. Proceedings of the ACM SIGIR 1999, pages 222-229.

[Berger 01] A. Berger. Statistical machine learning for information retrieval. Ph.D. dissertation, Carnegie Mellon University, 2001.

[Blei et al. 02] D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. In T G Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, Cambridge, MA, 2002. MIT Press.

[Cao et al. 05] Guihong Cao, Jian-Yun Nie, Jing Bai, Integrating word relationships into language models, Proceedings of ACM SIGIR 2005, Pages: 298 - 305.

[Carbonell and Goldstein 98]J. Carbonell and J. Goldstein, The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of SIGIR'98, pages 335--336.

[Chen & Goodman 98] S. F. Chen and J. T. Goodman. An empirical study of smoothing techniques for language modeling. Technical Report TR-10-98, Harvard University.

[Collins-Thompson & Callan 05] K. Collins-Thompson and J. Callan, Query expansing using random walk models, Proceedings of ACM CIKM 2005, pages 704-711.

[Cronen-Townsend et al. 02] Steve Cronen-Townsend, Yun Zhou, and W. Bruce Croft. Predicting query performance. In Proceedings of the ACM Conference on Research in Information Retrieval (SIGIR), 2002.

[Croft & Lafferty 03] W. B. Croft and J. Lafferty (ed), Language Modeling and Information Retrieval. Kluwer Academic Publishers. 2003.

[Fang et al. 04] H. Fang, T. Tao and C. Zhai, A formal study of information retrieval heuristics, Proceedings of ACM SIGIR 2004. pages 49-56.

References (cont.)

[Fang & Zhai 07] H. Fang and C. Zhai, Probabilistic models for expert finding, Proceedings of ECIR 2007.

[Fox 83] E. Fox. Expending the Boolean and Vector Space Models of Information Retrieval with P-Norm Queries and Multiple Concept Types. PhD thesis, Cornell University. 1983.

[Fuhr 01] N. Fuhr. Language models and uncertain inference in information retrieval. In Proceedings of the Language Modeling and IR workshop, pages 6--11.

[Gao et al. 04] J. Gao, J. Nie, G. Wu, and G. Cao, Dependence language model for information retrieval, In Proceedings of ACM SIGIR 2004.

[Good 53] I. J. Good. The population frequencies of species and the estimation of population parameters. Biometrika, 40(3 and 4):237--264, 1953.

[Greiff & Morgan 03] W. Greiff and W. Morgan, Contributions of Language Modeling to the Theory and Practice of IR, In W. B. Croft and J. Lafferty (eds), Language Modeling for Information Retrieval, Kluwer Academic Pub. 2003.

[Grossman & Frieder 04] D. Grossman and O. Frieder, Information Retrieval: Algorithms and Heuristics, 2nd Ed, Springer, 2004.

[He & Ounis 05] Ben He and Iadh Ounis, A study of the Dirichlet priors for term frequency normalisation, Proceedings of ACM SIGIR 2005, Pages 465 - 471

[Hiemstra & Kraaij 99] D. Hiemstra and W. Kraaij, Twenty-One at TREC-7: Ad-hoc and Cross-language track, In Proceedings of the Seventh Text REtrieval Conference (TREC-7), 1999.

[Hiemstra 01] D. Hiemstra. Using Language Models for Information Retrieval. PhD dissertation, University of Twente, Enschede, The Netherlands, January 2001.

[Hiemstra 02] D. Hiemstra. Term-specific smoothing for the language modeling approach to information retrieval: the importance of a query term. In Proceedings of ACM SIGIR 2002, 35-41

[Hiemstra et al. 04] D. Hiemstra, S. Robertson, and H. Zaragoza. Parsimonious language models for information retrieval, In Proceedings of ACM SIGIR 2004.

[Hofmann 99] T. Hofmann. Probabilistic latent semantic indexing. In Proceedings on the 22nd annual international ACM-SIGIR 1999, pages 50-57.

[Jarvelin & Kekalainen 02] Cumulated gain-based evaluation of IR techniques, ACM TOIS, Vol. 20, No. 4, 422-446, 2002.

[Jelinek 98] F. Jelinek, Statistical Methods for Speech Recognition, Cambirdge: MIT Press, 1998.

[Jelinek & Mercer 80] F. Jelinek and R. L. Mercer. Interpolated estimation of markov source parameters from sparse data. In E. S. Gelsema and L. N. Kanal, editors, Pattern Recognition in Practice. 1980. Amsterdam, North-Holland,.

References (cont.)

[Jeon et al. 03] J. Jeon, V. Lavrenko and R. Manmatha, Automatic Image Annotation and Retrieval using Cross-media Relevance Models, In Proceedings of ACM SIGIR 2003

[Jin et al. 02] R. Jin, A. Hauptmann, and C. Zhai, Title language models for information retrieval, In Proceedings of ACM SIGIR 2002.

[Kalt 96] T. Kalt. A new probabilistic model of text classication and retrieval. University of Massachusetts Technical report TR98-18,1996.

[Katz 87] S. M. Katz. Estimation of probabilities from sparse data for the language model component of a speech recognizer. IEEE Transactions on Acoustics, Speech and Signal Processing, volume ASSP-35:400--401.

[Kraaij et al. 02] W. Kraaij,T. Westerveld, D. Hiemstra: The Importance of Prior Probabilities for Entry Page Search. Proceedings of SIGIR 2002, pp. 27-34

[Kraaij 04] W. Kraaij. Variations on Language Modeling for Information Retrieval, Ph.D. thesis, University of Twente, 2004,

[Kurland & Lee 04] O. Kurland and L. Lee. Corpus structure, language models, and ad hoc information retrieval. In Proceedings of ACM SIGIR 2004.

[Kurland et al. 05] Oren Kurland, Lillian Lee, Carmel Domshlak, Better than the real thing?: iterative pseudo-query processing using cluster-based language models, Proceedings of ACM SIGIR 2005. pages 19-26.

[Kurland & Lee 05] Oren Kurland and Lillian Lee, PageRank without hyperlinks: structural re-ranking using links induced by language models, Proceedings of ACM SIGIR 2005. pages 306-313.

[Lafferty and Zhai 01a] J. Lafferty and C. Zhai, Probabilistic IR models based on query and document generation. In Proceedings of the Language Modeling and IR workshop, pages 1--5.

[Lafferty & Zhai 01b] J. Lafferty and C. Zhai. Document language models, query models, and risk minimization for information retrieval. In Proceedings of the ACM SIGIR 2001, pages 111-119.

[Lavrenko & Croft 01] V. Lavrenko and W. B. Croft. Relevance-based language models. In Proceedings of the ACM SIGIR 2001, pages 120-127.

[Lavrenko et al. 02] V. Lavrenko, M. Choquette, and W. Croft. Cross-lingual relevance models. In Proceedings of SIGIR 2002, pages 175-182.

[Lavrenko 04] V. Lavrenko, A generative theory of relevance. Ph.D. thesis, University of Massachusetts. 2004.

[Li & Croft 03] X. Li, and W.B. Croft, Time-Based Language Models, In Proceedings of CIKM'03, 2003

[Liu & Croft 02] X. Liu and W. B. Croft. Passage retrieval based on language models. In Proceedings of CIKM 2002, pages 15-19.

References (cont.)

[Liu & Croft 04] X. Liu and W. B. Croft. Cluster-based retrieval using language models. In Proceedings of ACM SIGIR 2004.

[MacKay & Peto 95] D. MacKay and L. Peto. (1995). A hierarchical Dirichlet language model. Natural Language Engineering, 1(3):289--307.

[Maron & Kuhns 60] M. E. Maron and J. L. Kuhns, On relevance, probabilistic indexing and information retrieval. Journal of the ACM, 7:216--244.

[McCallum & Nigam 98] A. McCallum and K. Nigam (1998). A comparison of event models for Naïve Bayes text classification. In AAAI-1998 Learning for Text Categorization Workshop, pages 41--48.

[Miller et al. 99] D. R. H. Miller, T. Leek, and R. M. Schwartz. A hidden Markov model information retrieval system. In Proceedings of ACM-SIGIR 1999, pages 214-221.

[Minka & Lafferty 03] T. Minka and J. Lafferty, Expectation-propagation for the generative aspect model, In Proceedings of the UAI 2002, pages 352--359.

[Nallanati & Allan 02] Ramesh Nallapati and James Allan, Capturing term dependencies using a language model based on sentence trees. In Proceedings of CIKM 2002. 383-390

[Nallanati et al 03] R. Nallanati, W. B. Croft, and J. Allan, Relevant query feedback in statistical language modeling, In Proceedings of CIKM 2003.

[Ney et al. 94] H. Ney, U. Essen, and R. Kneser. On Structuring Probabilistic Dependencies in Stochastic Language Modeling. Comput. Speech and Lang., 8(1), 1-28.

[Ng 00]K. Ng. A maximum likelihood ratio information retrieval model. In Voorhees, E. and Harman, D., editors, Proceedings of the Eighth Text REtrieval Conference (TREC-8), pages 483--492. 2000.

[Ogilvie & Callan 03] P. Ogilvie and J. Callan Combining Document Representations for Known Item Search. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2003), pp. 143-150

[Ponte & Croft 98]] J. M. Ponte and W. B. Croft. A language modeling approach to information retrieval. In Proceedings of ACM-SIGIR 1998, pages 275-281.

[Ponte 98] J. M. Ponte. A language modeling approach to information retrieval. Phd dissertation, University of Massachusets, Amherst, MA, September 1998.

References (cont.)

[Ponte 01] J. Ponte. Is information retrieval anything more than smoothing? In Proceedings of the Workshop on Language Modeling and Information Retrieval, pages 37-41, 2001.

[Robertson & Sparch-Jones 76] S. Robertson and K. Sparck Jones. (1976). Relevance Weighting of Search Terms. JASIS, 27, 129-146.

[Robertson 77] S. E. Robertson. The probability ranking principle in IR.Journal of Documentation, 33:294-304, 1977.

[Robertson & Walker 94] S. E. Robertson and S. Walker, Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval. Proceedings of ACM SIGIR 1994. pages 232-241. 1994.

[Rosenfeld 00] R. Rosenfeld, Two decades of statistical language modeling: where do we go from here? In Proceedings of IEEE, volume~88.

[Salton et al. 75] G. Salton, A. Wong and C. S. Yang, A vector space model for automatic indexing. Communications of the ACM, 18(11):613--620.

[Salton & Buckley 88] G. Salton and C. Buckley, Term weighting approaches in automatic text retrieval, Information Processing and Management, 24(5), 513-523. 1988.

[Shannon 48] Shannon, C. E. (1948).. A mathematical theory of communication. Bell System Tech. J. 27, 379-423, 623-656.

[Shen et al. 05] X. Shen, B. Tan, and C. Zhai. Context-sensitive information retrieval with implicit feedback. In Proceedings of ACM SIGIR 2005.

[Si et al. 02] L. Si , R. Jin, J. Callan and P.l Ogilvie. A Language Model Framework for Resource Selection and Results Merging. In Proceedings of the 11th International Conference on Information and Knowledge Management (CIKM) . 2002

[Singhal et al. 96] A. Singhal, C. Buckley, and M. Mitra, Pivoted document length normalization, Proceedings of ACM SIGIR 1996.

[Singhal 01] A. Singhal, Modern Information Retrieval: A Brief Overview. Amit Singhal. In IEEE Data Engineering Bulletin 24(4), pages 35-43, 2001.

[Song & Croft 99] F. Song and W. B. Croft. A general language model for information retrieval. In Proceedings of Eighth International Conference on Information and Knowledge Management (CIKM 1999)

[Sparck Jones 72] K. Sparck Jones, A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation 28, 11-21, 1972 and 60, 493-502, 2004.

References (cont.)

[Sparck Jones et al. 00] K. Sparck Jones, S. Walker, and S. E. Robertson, A probabilistic model of information retrieval: development and comparative experiments - part 1 and part 2. Information Processing and Management, 36(6):779--808 and 809--840.

[Sparck Jones et al. 03] K. Sparck Jones, S. Robertson, D. Hiemstra, H. Zaragoza, Language Modeling and Relevance, In W. B. Croft and J. Lafferty (eds), Language Modeling for Information Retrieval, Kluwer Academic Pub. 2003.

[Srikanth & Srihari 03] M. Srikanth, R. K. Srihari. Exploiting Syntactic Structure of Queries in a Language Modeling Approach to IR. in Proceedings of Conference on Information and Knowledge Management(CIKM'03).

[Srikanth 04] M. Srikanth. Exploiting query features in language modeling approach for information retrieval. Ph.D. dissertation, State University of New York at Buffalo, 2004.

[Tan et al. 06] Bin Tan, Xuehua Shen, and ChengXiang Zhai,, Mining long-term search history to improve search accuracy, Proceedings of ACM KDD 2006.

[Tao et al. 06] Tao Tao, Xuanhui Wang, Qiaozhu Mei, and ChengXiang Zhai, Language model information retrieval with document expansion, Proceedings of HLT/NAACL 2006.

[Tao & Zhai 06] Tao Tao and ChengXiang Zhai, Regularized estimation of mixture models for robust pseudo-relevance feedback. Proceedings of ACM SIGIR 2006.

[Turtle & Croft 91]H. Turtle and W. B. Croft, Evaluation of an inference network-based retrieval model. ACM Transactions on Information Systems, 9(3):187--222.

[van Rijsbergen 86] C. J. van Rijsbergen. A non-classical logic for information retrieval. The Computer Journal, 29(6).

[Witten et al. 99] I.H. Witten, A. Mo#at, and T.C. Bell. Managing Gigabytes - Compressing and Indexing Documents and Images. Academic Press, San Diego, 2nd edition, 1999.

[Wong & Yao 89] S. K. M. Wong and Y. Y. Yao, A probability distribution model for information retrieval. Information Processing and Management, 25(1):39--53.

[Wong & Yao 95] S. K. M. Wong and Y. Y. Yao. On modeling information retrieval with probabilistic inference. ACM Transactions on Information Systems, 13(1):69--99.

[Xu & Croft 99] J. Xu and W. B. Croft. Cluster-based language models for distributed retrieval. In Proceedings of the ACM SIGIR 1999, pages 15-19,

[Xu et al. 01] J. Xu, R. Weischedel, and C. Nguyen. Evaluating a probabilistic model for cross-lingual information retrieval. In Proceedings of the ACM-SIGIR 2001, pages 105-110.

References (cont.)

[Zaragoza et al. 03] Hugo Zaragoza, D. Hiemstra and M. Tipping, Bayesian extension to the language model for ad hoc information retrieval. In Proceedings of SIGIR 2003: 4-9.

[Zhai & Lafferty 01a] C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to ad hoc information retrieval. In Proceedings of the ACM-SIGIR 2001, pages 334-342.

[Zhai & Lafferty 01b] C. Zhai and J. Lafferty. Model-based feedback in the language modeling approach to information retrieval, In Proceedings of the Tenth International Conference on Information and Knowledge Management (CIKM 2001).

[Zhai & Lafferty 02] C. Zhai and J. Lafferty. Two-stage language models for information retrieval. In Proceedings of the ACM-SIGIR 2002, pages 49-56.

[Zhai et al. 03] C. Zhai, W. Cohen, and J. Lafferty, Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval, In Proceedings of ACM SIGIR 2003.

[Zhai & Lafferty 06] C. Zhai and J. Lafferty, A risk minimization framework for information retrieval, Information Processing and Management, 42(1), Jan. 2006, pages 31-55.

[Zhai 02] C. Zhai, Language Modeling and Risk Minimization in Text Retrieval, Ph.D. thesis, Carnegie Mellon University, 2002.

[Zhai & Lafferty 06] C. Zhai and J. Lafferty, A risk minimization framework for information retrieval, Information Processing and Management, 42(1), Jan. 2006, pages 31-55.

[Zhang et al. 02] Y. Zhang , J. Callan, and Thomas P. Minka, Novelty and redundancy detection in adaptive filtering. In Proceedings of SIGIR 2002, 81-88

Roadmap

- This lecture: systematic review of language models for IR
- Next lecture: formal retrieval frameworks

Download Presentation

Connecting to Server..