Georg Buscher

Georg Buscher

German Research Center for Artificial Intelligence (DFKI)

Knowledge Management Department

Kaiserslautern, Germany

SIGIR 07 Doctoral Consortium

Attention-Based

Information Retrieval

Georg Buscher

Motivation

Magnetic Resonance Imaging uses magnetic fields and radio waves to produce high quality two- or three-dimensional images of brain structures. Sensors read frequencies of radio waves and a computer uses the information to construct an image of the brain (see 2) .

Positron Emission Tomography measures emissions from radioactively labeled metabolically active chemicals that have been injected into the bloodstream. The emission data are computer-processed to produce 2- or 3-dimensional images of the distribution of the chemicals throughout the brain. Especially useful are a wide array of chemicals used to map different aspects of neurotransmitter activity (see 3).

Homer's personality is one of frequent stupidity, laziness, and explosive anger. He also suffers from a short attention span which complements his intense but short-lived passion for hobbies, enterprises and various causes. Furthermore, he is prone to emotional outbursts.

1 2 3

Georg Buscher

Outline

Acquiring attention evidence– Attention evidence through eye tracking

– Attention annotation and derivation with Dempster-Shafer

Applications in Information Retrieval– Attention-based TfIdf

– Context elicitation

– Context-based Index

– Query Expansion / result re-ranking

Georg Buscher

Sources of Attention-Data

There are many indications of attention from the user:

read

skimmed

longer viewed

Annotations (explicit)

Reading evidence (implicit)

Georg Buscher

Reading Detection – An Example

Georg Buscher

Attention Annotations Imply Different Levels of Attention

Attention evidence values

[0.7; 1.0] [0.5; 1.0] [0.2; 0.7][1.0; 1.0] … … …

Range from 0 to 1

Width of an interval expresses uncertainty

Georg Buscher

Dempster-Shafer Combination of Attention Evidence

read

[The demo … provide][different][visualizations][and interfaces][according … situation.]

R R H R H U R U R

[0.5; 1] [0.85; 1] [0.96; 1] [0.85; 1] [0.5; 1]

Calculate one value of attention (att(t) = bel(t) – 0.2*bel(t) + 0.2*pl(t)):

0.6 0.88 0.97 0.88 0.6

In that way, the function att provides an attention value for every term of the document.

attdifferent, d = 0.88

attaccording, d = 0.6

attsomethingElse, d = 0

Georg Buscher

Outline

Acquiring attention evidence– Attention evidence through eye tracking

– Attention annotation and derivation with Dempster-Shafer

Applications in Information Retrieval– Attention-based TfIdf Desktop Index

– Context elicitation

– Context-based Index

– Query Expansion

Georg Buscher

Attention-Based Desktop Index

A Desktop index is especially for re-finding known documents. You can better remember those parts of a document that you paid

attention to. Attended terms should be weighted higher.

TfIdf-based modification– Attention is a local factor (like tf)– The higher the maximal intensity of an attended document part, the

more weight should be assigned to the attention value.– The lower the maximal intensity of an attended document part, the

more weight should be assigned to tf.

attention part term frequency part

tft,d : term frequency of term t in document dattt,d : attention value of term t in document d

α in [0; 1] is a balancing factor for defining the influence of attention in contrast to term frequency.

Georg Buscher

Why Context? The Search for the Mental Model

If a knowledge worker tries to recall something concerning a topic,does he primarily think

– on the basis of documents and document structures or

– on the basis of former thematic contexts?

Rather the latter…

While re-finding some information, one does not search primarily for the document, but for the former mental model.Documents mediate.

Georg Buscher

Elicitation and Representation of the Thematic Context

Document 1

Brain imaging

Document 2

Brain imaging

Document 3

The Simpsons

thematic context

Brain imaging

Some read sub-documents

Combination of the viewed sub-documents to one virtual context document (only those attended parts that have a thematic overlapping)

Document 4

Brain imaging

Georg Buscher

Determination of Thematical Overlapping

Determine buzzwords for each viewed document by using– Attention value

– Idf of desktop index

Compare buzzword vector with previous context vectors– If there is a similarity, then merge with context vector

– Else buzzword vector is a new context

? Previouscontexts

Currentlyviewed

document(part)

Georg Buscher

Idea: two indexes

1. Term – Context 2. Context – Document

A context is represented by a virtual context document The value for each term–context relation is influenced by the degree of attention

Context-Based Vector-Space Index

Common index structure Doc1 Doc2 Doc3

Term1

Term2

Term3

2

3

1

0

1

0

4

0

1

C1 C2 C3

Term1

Term2

Term3

Term4

5

2

0

1

2

1

0

3

1

2

1

3

Doc1 Doc2

C1

C2

C3

x

x

x

x

Georg Buscher

New Kinds of Search Tasks Possible

Local search:Find for the current task (parts of) documents,that I formerly used for a similar task.

Enterprise-wide search:Find for the current task (parts of) documents,that I do not know yet, butthat have been used by some colleague for a similar task.

Georg Buscher

Evaluation of the Context-Based Index

Main advantage is expected to show up in several weeks.

Not possible to do real-world eye tracking studies for such a long time

Artificial experiment:– Several different exploration

tasks within some hours

– Then some re-finding tasks about previously viewed content

– Measuring the time or user-satisfaction during the search process?

Context-based search

Normal search

Georg Buscher

Contextual Attention-Based Relevance Feedback

Problem with context-based index: it doesn’t scale for web search therefore query expansion

Current elicited context (i.e. term vector) expresses current interest of the user

Topmost characteristic keywords will be used for query expansion

Georg Buscher

Attention datageneration module

Eye Tracker

Text MarkRecognition Attention-annotated

document

The Global Picture

Attention-baseddesktop index

Context-basedindex

Context document

Query expansionfor web search

attention

Thank youfor your

!attention