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Attention-Based Information Retrieval. Georg Buscher German Research Center for Artificial Intelligence (DFKI) Knowledge Management Department Kaiserslautern, Germany. SIGIR 07 Doctoral Consortium. Motivation.

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Georg Buscher German Research Center for Artificial Intelligence (DFKI)


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slide1

Attention-Based

Information Retrieval

Georg Buscher

German Research Center for Artificial Intelligence (DFKI)

Knowledge Management Department

Kaiserslautern, Germany

SIGIR 07 Doctoral Consortium

motivation
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) .

1

2

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.
  • 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).
outline
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
sources of attention data
Sources of Attention-Data
  • There are many indications of attention from the user:

Reading evidence (implicit)

read

Annotations (explicit)

skimmed

longer viewed

attention annotations imply different levels of attention
Attention Annotations Imply Different Levels of Attention
  • Attention evidence values

[1.0; 1.0]

[0.7; 1.0]

[0.5; 1.0]

[0.2; 0.7]

  • Range from 0 to 1
  • Width of an interval expresses uncertainty
dempster shafer combination of attention evidence
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

outline1
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
attention based desktop index
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 d

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

attt,d : attention value of term t in document d

why context the search for the mental model
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.
elicitation and representation of the thematic context
Elicitation and Representation of the Thematic Context

Document 1

Brain imaging

Document 2

Brain imaging

Document 3

The Simpsons

Document 4

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)

thematic context

Brain imaging

determination of thematical overlapping
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

Currentlyvieweddocument(part)

?

Previouscontexts

context based vector space index
Context-Based Vector-Space Index
  • Common index structure

Doc1 Doc2 Doc3

Term1

Term2

Term3

0

1

0

4

0

1

2

3

1

  • Idea: two indexes1. 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

C1 C2 C3

Doc1 Doc2

Term1

Term2

Term3

Term4

2

1

0

3

1

2

1

3

5

2

0

1

C1

C2

C3

x

x

x

x

new kinds of search tasks possible
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.
evaluation of the context based index
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

contextual attention based relevance feedback
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
the global picture
The Global Picture

Eye Tracker

Attention data

generation module

Attention-baseddesktop index

Text Mark

Recognition

Attention-annotated document

Context-basedindex

Thank youfor your

Context document

attention

attention

!

Query expansionfor web search