Conversation Clusters:
Grouping Conversation Through Human Computer Dialog




                      Tony Bergstrom and Ka...
Scenario
Scenario
As a group were discussing a new design problem.
Charlie suggests using red circles.

... Lucy shoots it down, sa...
Scenario
As a group were discussing a new design problem.
Charlie suggests using red circles.

... Lucy shoots it down, sa...
Scenario
As a group were discussing a new design problem.
Charlie suggests using red circles.

... Lucy shoots it down, sa...
Scenario
As a group were discussing a new design problem.
Charlie suggests using red circles.

... Lucy shoots it down, sa...
Speech recognition is not perfect
   20-30% word error rate in normal conditions
                                  [Muntea...
Speech recognition is not perfect
   20-30% word error rate in normal conditions
                                  [Muntea...
Human-Computer Dialog
Techniques to identify salient moments in unstructured transcripts
by leveraging human knowledge wit...
Capture Words
Cluster Words
Edit Clusters
Forms of Input

Speech Transcription Software
Participant Tactile Feedback
Wikipedia / Explicit Semantic Analysis (ESA)
[G...
Generating Clusters with
Explicit Semantic Analysis (ESA)
  Query: ‘forest preserves in Utah’
   1 U.S. National Monument ...
Generating Clusters with
Explicit Semantic Analysis (ESA)
  Query: ‘forest preserves in Utah’
   1 U.S. National Monument ...
Generating Clusters with
          Explicit Semantic Analysis (ESA)
               Query: ‘forest preserves in Utah’
1 U.S...
Eliminating Redundant Topics
(102, Colorado)(48, Wilderness)(32, Forest)(28, Mountain)(27, Juan)...
(97, Utah)(37, Canyon)...
Eliminating Redundant Topics
(102, Colorado)(48, Wilderness)(32, Forest)(28, Mountain)(27, Juan)...
(97, Utah)(37, Canyon)...
Eliminating Redundant Topics
(102, Colorado)(48, Wilderness)(32, Forest)(28, Mountain)(27, Juan)...
(97, Utah)(37, Canyon)...
Timeline Generation


         utahs canyons   colorados park   mountain lake            sandstone




                   ...
Timeline Generation


         utahs canyons   colorados park   mountain lake            sandstone




                   ...
Timeline Generation


         utahs canyons   colorados park   mountain lake            sandstone




                   ...
Timeline Generation


         utahs canyons   colorados park   mountain lake            sandstone




                   ...
BIN 1      BIN 2       BIN 3
                        Segment 5
            Segment 3
Segment 1

            Segment 4   Se...
BIN 1      BIN 2       BIN 3
                        Segment 5
            Segment 3
Segment 1

            Segment 4   Se...
Word Selection in Timeline
Word Selection in Timeline

                   trail        bikes        bikes
      trail
                               ...
Word Selection in Timeline

      trail        trail        bikes      bikes
                   bikes        motorcycle mo...
Prototype Contributions



1. Dynamic algorithm to learn conversation models
2. Conversation discourse models
3. Track the...
Questions


Tony Bergstrom and Karrie Karahalios
University of Illinois at Urbana-Champaign
{abergst2, kkarahal}@cs.uiuc.e...
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Conversation Clusters: Grouping Conversation Through Human Computer Dialog

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Conversation Clusters: Grouping Conversation Through Human Computer Dialog

  1. 1. Conversation Clusters: Grouping Conversation Through Human Computer Dialog Tony Bergstrom and Karrie Karahalios University of Illinois at Urbana-Champaign
  2. 2. Scenario
  3. 3. Scenario As a group were discussing a new design problem. Charlie suggests using red circles. ... Lucy shoots it down, says “That’s stupid” ... 5 minutes before the end of the meeting, Lucy says, “I’ve got it! I’m a genius ... red circles!”
  4. 4. Scenario As a group were discussing a new design problem. Charlie suggests using red circles. ... Lucy shoots it down, says “That’s stupid” ... 5 minutes before the end of the meeting, Lucy says, “I’ve got it! I’m a genius ... red circles!”
  5. 5. Scenario As a group were discussing a new design problem. Charlie suggests using red circles. ... Lucy shoots it down, says “That’s stupid” ... 5 minutes before the end of the meeting, Lucy says, “I’ve got it! I’m a genius ... red circles!”
  6. 6. Scenario As a group were discussing a new design problem. Charlie suggests using red circles. ... Lucy shoots it down, says “That’s stupid” ... 5 minutes before the end of the meeting, Lucy says, “I’ve got it! I’m a genius ... red circles!”
  7. 7. Speech recognition is not perfect 20-30% word error rate in normal conditions [Munteanu 2006] Natural language processing is hard [Rosenfeld 2000]
  8. 8. Speech recognition is not perfect 20-30% word error rate in normal conditions [Munteanu 2006] Natural language processing is hard [Rosenfeld 2000]
  9. 9. Human-Computer Dialog Techniques to identify salient moments in unstructured transcripts by leveraging human knowledge with the computational affordances of computers er o i o n s h ta u re co du s tto at v st utahs canyons colorados park mountain lake sandstone ne nw s ri ra oo ct i wi nc s d on h ld n ch l er ai w i l b i t a t s o sio n ra n ev ada tr ne d er n ess canyons trail mountain bikes wilderness hikers ss er p ar er o s ut a nevada k n io h ion os rid ha at v s er er ne s m ot s deputy rights motorcyclists atvs n es v ad u t ah p ar k orb r il d es a ut ca tr a w ilr a ah ny ik e i ls t t on r es ar l an nevada ch fo ds es r e bik es cr va ad a p ar k ea park montana bitterroot ti o a n edv n ne al ne cr o e r o s i on
  10. 10. Capture Words
  11. 11. Cluster Words
  12. 12. Edit Clusters
  13. 13. Forms of Input Speech Transcription Software Participant Tactile Feedback Wikipedia / Explicit Semantic Analysis (ESA) [Gabriovich 06]
  14. 14. Generating Clusters with Explicit Semantic Analysis (ESA) Query: ‘forest preserves in Utah’ 1 U.S. National Monument (732.787) 2 Utah Lake (646.047) 3 United States Forest Service (584.821) 4 Price, Utah (575.731) 5 Red Deer (469.844) 6 Colorado (453.202) 7 Protected areas of the United States (452.932) 8 Utah (451.928) 9 Western United States (431.971) 10 Utah County, Utah (427.949)
  15. 15. Generating Clusters with Explicit Semantic Analysis (ESA) Query: ‘forest preserves in Utah’ 1 U.S. National Monument (732.787) 2 Utah Lake (646.047) 3 United States Forest Service (584.821) 4 Price, Utah (575.731) 5 Red Deer (469.844) 6 Colorado (453.202) 7 Protected areas of the United States (452.932) 8 Utah (451.928) 9 Western United States (431.971) 10 Utah County, Utah (427.949)
  16. 16. Generating Clusters with Explicit Semantic Analysis (ESA) Query: ‘forest preserves in Utah’ 1 U.S. National Monument (732.787) (26.6, utah) (25.7, forest) (13.6, preserv) (60.4, utah) (13.6, preserv) 2 Utah Lake (646.047) 3 United States Forest Service (584.821) 4 Price, Utah (575.731) 5 Red Deer (469.844) 6 Colorado (453.202) 7 Protected areas of the United States (452.932) 8 Utah (451.928) 9 Western United States (431.971) 10 Utah County, Utah (427.949)
  17. 17. Eliminating Redundant Topics (102, Colorado)(48, Wilderness)(32, Forest)(28, Mountain)(27, Juan)... (97, Utah)(37, Canyon)(36, Sandstone)(25, Mountain)(21, Colorado)... (54, Colorado)(44, Canyon)(36, Sandstone)(22, Utah)(20, Mountain) (34, Utah)(32, Montana)(30, Colorado)(22, Mountain)(18, Forest)... (61, Canyon)(32, Colorado)(32, Utah)(26, Trail)(19, Forest)... ...
  18. 18. Eliminating Redundant Topics (102, Colorado)(48, Wilderness)(32, Forest)(28, Mountain)(27, Juan)... (97, Utah)(37, Canyon)(36, Sandstone)(25, Mountain)(21, Colorado)... (54, Colorado)(44, Canyon)(36, Sandstone)(22, Utah)(20, Mountain) (34, Utah)(32, Montana)(30, Colorado)(22, Mountain)(18, Forest)... (61, Canyon)(32, Colorado)(32, Utah)(26, Trail)(19, Forest)... ...
  19. 19. Eliminating Redundant Topics (102, Colorado)(48, Wilderness)(32, Forest)(28, Mountain)(27, Juan)... (97, Utah)(37, Canyon)(36, Sandstone)(25, Mountain)(21, Colorado)... (60, Utah)(40, Canyon)(37, Colorado)(36, Sandstone)(23, Mountain)... (54, Colorado)(44, Canyon)(36, Sandstone)(22, Utah)(20, Mountain) (34, Utah)(32, Montana)(30, Colorado)(22, Mountain)(18, Forest)... (61, Canyon)(32, Colorado)(32, Utah)(26, Trail)(19, Forest)... ... er o i o n s h ta u re co du s tto at v st ne nw s ri ra oo ct i nc s d on h n ch w i l b i t a t s o sio n ra n ev ada d er n ess er p ar er o s u nevada tah k n si o ion ha o at v s er ne s m ot n es v ad u t ah p ar k orb r il d es a ut c tr a a h any w ilr a ik e i ls t t on r es ar l an nevada ch fo ds es r e bik es cr va ad a p ar k ea park ti o a n edv n ne al ne cr o e r o s i on
  20. 20. Timeline Generation utahs canyons colorados park mountain lake sandstone wi ld l er ai tr ne canyons trail mountain bikes wilderness hikers ss rid er s deputy rights motorcyclists atvs montana bitterroot
  21. 21. Timeline Generation utahs canyons colorados park mountain lake sandstone wi ld l er ai tr ne canyons trail mountain bikes wilderness hikers ss rid er s deputy rights motorcyclists atvs montana bitterroot
  22. 22. Timeline Generation utahs canyons colorados park mountain lake sandstone wi ld l er ai tr ne canyons trail mountain bikes wilderness hikers ss rid er s deputy rights motorcyclists atvs montana bitterroot
  23. 23. Timeline Generation utahs canyons colorados park mountain lake sandstone wi ld l er ai tr ne canyons trail mountain bikes wilderness hikers ss rid er s deputy rights motorcyclists atvs montana bitterroot
  24. 24. BIN 1 BIN 2 BIN 3 Segment 5 Segment 3 Segment 1 Segment 4 Segment 6 Segment 2 1 3 6 Topic Topic Topic 2 4 7 Topic Topic Topic 5 8 Topic Topic
  25. 25. BIN 1 BIN 2 BIN 3 Segment 5 Segment 3 Segment 1 Segment 4 Segment 6 Segment 2 1 3 6 Topic Topic Topic 2 4 7 Topic Topic Topic 5 8 Topic Topic
  26. 26. Word Selection in Timeline
  27. 27. Word Selection in Timeline trail bikes bikes trail motorcycle atvs bikes motorcycle wilderness riders wilderness atvs trail trail vehicles riders atvs riders recreation mountain wilderness wilderness trail park wildlife park wildlife colorado mountain mountain 4 78
  28. 28. Word Selection in Timeline trail trail bikes bikes bikes motorcycle motorcycle atvs atvs wilderness riders wilderness trail trail vehicles riders atvs riders recreation mountain wilderness wilderness park trail wildlife park wildlife colorado mountain mountain 4 78
  29. 29. Prototype Contributions 1. Dynamic algorithm to learn conversation models 2. Conversation discourse models 3. Track thematic changes and idea formation 4. Access prior conversation content in near real time
  30. 30. Questions Tony Bergstrom and Karrie Karahalios University of Illinois at Urbana-Champaign {abergst2, kkarahal}@cs.uiuc.edu

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