Positional Relevance Model - PowerPoint Presentation

Slide1

Positional Relevance Model for Pseudo–Relevance Feedback

Yuanhua Lv & ChengXiang ZhaiDepartment of Computer Science, UIUC

Presented by Bo Man

2014/11/18Slide2

Positional Relevance Model

for Pseudo–Relevance Feedback

Yuanhua

Lv

&

ChengXiang ZhaiDepartment of Computer Science, UIUC

Presented by Bo Man

2014/11/18Slide3

Presentation Guideline

Review on Feedback ✨✨Motivation

✨✨✨✨

Positional Relevance Model ✨✨✨✨✨Experiments, Results and Analyses ✨✨✨✨

Conclusions

✨✨Slide4

Presentation Guideline

Review on Feedback ✨✨

Motivation

✨✨✨✨Positional Relevance Model ✨✨✨✨✨Experiments

,

Results and Analyses

✨✨✨✨

Conclusions

✨✨Slide5

Review on Feedback ✨✨

(1)Explicit Feedback

Easy

for

trainingBut need

user’s

interactionSlide6

Review on Feedback ✨✨

(2)Implicit Feedback

Not need

interaction

But

work more

on

miningSlide7
Slide8
Slide9
Slide10
Slide11

(3)Pseudo

-

Relevance

Feedback

No

need

user’s

interaction

No

MiningSlide12

Problems?

Traditional Pseudo-Relevance Feedback assumes that the

contents

of

a document are

incoherent

(sharing

the

same

topic).

What

if

a document

shares different

topics？

Term-based? Or

document-based?Slide13

Presentation Guideline

Review on Feedback ✨✨

Motivation

✨✨✨✨Positional Relevance Model ✨✨✨✨✨Experiments,

Results and Analyses

✨✨✨✨

Conclusions

✨✨Slide14

Presentation Guideline

Review on Feedback ✨✨

Motivation

✨✨✨✨Positional Relevance Model ✨✨✨✨✨

Experiments

,

Results and Analyses

✨✨✨✨

Conclusions

✨✨Slide15

Motivation ✨✨✨✨

How to effectively select from feedback documents t

he

words

that are focused on

the

query

topic

based

on

positions

of

terms

in feedback

documents?Slide16

Presentation Guideline

Review on Feedback ✨✨

Motivation

✨✨✨✨Positional Relevance Model ✨✨✨✨✨Experiments,

Results and Analyses

✨✨✨✨

Conclusions

✨✨Slide17

Presentation Guideline

Review on Feedback ✨✨

Motivation

✨✨✨✨Positional Relevance Model ✨✨✨✨✨

Experiments

,

Results and Analyses

✨✨✨✨

Conclusions

✨✨Slide18

Positional Relevance

Model ✨✨✨✨✨Relevance Model (one of the most robust)

Θ

rep- resent the set of smoothed document models for the pseudo feedback documents

.

p(

θD) is a prior on documents and is often assumed to be uniform without any additional prior knowledge about document D. After the relevance model is estimated, the estimated P (w|Q) can then be interpolated with the original query model θQ to improve

performance

.

α is a parameter to control the amount of feedback. Slide19

Positional Relevance Model(PRM)i

indicates a position in document D F is the set of feedback documents (assumed to be relevant) Challenge? How to

estimate

joint

probability?

Positional

Relevance

Model

✨

✨✨✨✨Slide20

Methods(1)

i.i.d. Sampling(2) conditional Samplingestimating P (w, Q, D,

i

)

Positional

Relevance

Model ✨✨✨✨✨Slide21

i.i.d. sampling

Positional Relevance Model ✨✨✨✨✨Slide22

i.i.d. sampling derivation(1)

P(D) can be interpreted as a document prior and set to a uniform distribution with no prior knowledge about document D. assume that every position is equally likely but

it is possible to estimate P(

i|D

) based on document

structures

assume that the generation of word w and that of query Q are independent

Positional

Relevance

Model

✨

✨✨✨✨Slide23

i.i.d. sampling derivation

(2)In the above equation, P (w|D, i) is the probability of sampling word w at position i in document D. To improve the efficiency of PRM, we simplify P (w|D, i

) as:

QUESTION: HOW to

estimate

?

The

query

likelihood

at

position

i

of

document D.

Positional

Relevance

Model

✨✨✨✨✨Slide24

conditional sampling

……QUESTION:

HOW

to

estimate ?The query

likelihood

at

position

i

of

document

D.

Positional

Relevance

Model ✨

✨✨✨✨Slide25

estimate the

query likelihood at position i of document

D

(1)

Use

Positional Language Model(2)Use

Gaussian

kernel

function

(3)Approximate

(4)Set

parameters

Positional

Relevance

Model

✨

✨✨✨✨Slide26

estimate the

query likelihood at position i of document

D

(5)

Use

JM Smoothing(6)Compute

The

computation

of

positional

query

likelihood

is

the

most time-consuming

part

in

estimating

PRM.

Positional

Relevance

Model

✨

✨✨✨✨Slide27

Presentation Guideline

Review on Feedback ✨✨

Motivation

✨✨✨✨Positional Relevance Model ✨✨✨✨✨Experiments,

Results and Analyses

✨✨✨✨

Conclusions

✨✨Slide28

Presentation Guideline

Review on Feedback ✨✨

Motivation

✨✨✨✨Positional Relevance Model ✨✨✨✨✨

Experiments

,

Results and Analyses

✨✨✨✨

Conclusions

✨✨Slide29

Experiments and

Results ✨✨✨✨Evaluation methods.

(

1) The basic retrieval model is the

KL-divergence retrieval

model

, and we chose the Dirichlet smoothing method [33] for smoothing document language models, where the smoothing parameter μ was set empirically to 1500. This method was labeled as “NoFB”.(2) The baseline pseudo feedback method is

the relevance model “RM3

”

,

which is one of the most effective and robust pseudo feedback methods un- der language modeling

framework.

(

3) Another baseline pseudo feedback method is a

standard passage-based feed- back model, labeled as “RM3-p”

, which estimates the RM3 relevance model based on the best matching passage of each feedback

document.

(

4) We have two variations of PRM, i.e.,

“PRM1” and “PRM2”

, which are based on the two

estimation methods described in Section 3.2, respectively. (5) In addition, we also used

PRM1 and PRM2 for passage feed- back in a way as RM3-p does. Specifically, we first computed a PLM for each position of the document, and then we estimate

a PRM based on a passage of size 2σ centered at the position with the maximum positional query likelihood score Slide30

Results

Experiments and Results ✨✨✨✨Slide31

More results

Robustness Analysis Experiments and Results

✨✨✨✨Slide32

Presentation Guideline

Review on Feedback ✨✨

Motivation

✨✨✨✨Positional Relevance Model ✨✨✨✨✨Experiments,

Results and Analyses

✨✨✨✨

Conclusions

✨✨Slide33

Presentation Guideline

Review on Feedback ✨✨

Motivation

✨✨✨✨Positional Relevance Model ✨✨✨✨✨

Experiments

,

Results and Analyses

✨✨✨✨

Conclusions

✨✨Slide34

Propose a

novel positional relevance model(PRM)PRM exploits

term

position

and proximity to assign

more

weights

to

words

closer

to

query

words, based on the

intuition—words closer

to query

words are more

likely

to be

consistent

with

the

query

topic.

Experiments

results

show

that

PRM

is

quite

effective

and

performs

significantly

better

than

others

based

on

document

or

passage.

Conclusions

✨

✨Slide35

Questions?