We are transitioning from the programmatic to the cognitive computing era.IBM Deep Blue won against the world champion in Chess 1996. IBM Watson won against the two world champions in the famous US quiz show "Jeopardy" 2011. Since then, the press heavily established the term "Cognitive Computing" to the public. I will explain how IBM Watson works internally and start with Algebraic Text Extraction. DeepQA is the heart of IBM Watson and I will explain each component of this pipeline, the linguistic preprocessor, hypothesis generation, hypothesis and evidence scoring, final matching based on supervised learning and confidence estimation. Finally, I conclude with an overview of actual use cases and outline the roadmap of future work.

1. Swiss Group for Artificial Intelligence and Cognitive Science
Intelligent Systems and Applications Workshop 2014, University of Basel
Watson Technical Deep Dive
© 2013 IBM Corporation
@RomeoKienzler, IBM Innovation Center Zurich

2. (part of) my role @IBM
● Accelerate cognitive computing
● In Switzerland
● Through
● Academia
● Startups/ISV's
● Cloud
Watson in the cloud: bit.ly/go4bluemix
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3. What Watson is not
● Search Engine
● Database System
● HAL9000
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4. 4 © 2013 IBM Corporation

5. What Watson is
● Cognitive System (Marketing)
● Combination of
● Information Retrieval
● NLP
● Structured + Unstructured Data !
● Runs on UIMA
● Based on supervised learning
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6. What is a parser?
● Annotate sentence with
● Tags
● Relationships
● Probabilistic (e.g. Stanford)
● Rule based
● (E) Slot Grammar
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7. Slot Grammar
● Simplified
● Lexicalist character
● High focus on words
● Low focus on structure
● Assign words to slots
I can resist everything except temptation
● Subject (I)
● Verb (can resist)
● Object (everything except temptation)
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8. PAS - Builder
● Predicate-Argument Structure
● Downstream to ESG
● Reduces complexity of ESG
“John opened Bill's door (with his key)
John's key opened Bill's door
Bill's door opened
Bill's door was opened (by John)”
OPEN (John door key)
| | |
Agent Theme Instrument
Many ESG trees reduce to same PAS
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9. Relationships
● Relationship Extractor
● Combination of
● Manual pattern specifications
~30 types, high precision
● Statistical methods
~7000 types, low precision
● SVM's on DBPedia/Wikipedia
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10. Relationships (2)
“The Screwtape Letters” from a senior devil to
an under devil are by this man better known for
children’s books
author(“this man”,“The Screwtape Letters”)
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11. Ingestion
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12. Ingestion
● Corpus creation
● Input format: TREC
(Text Retrieval Conference)
● Multiple HTML pages in one
HDFS file
● Parallel ingestion process
(LiteScale)
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13. Dictionary
● started w/ Wikipedia copus
● Keyword → Text structure
● Transformation of free text
● into Keyword → Text
● optimization objective
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14. Knowledge Expansion
● Follow links in content
● Identify content keywords and link
to new content
● → generate more content in
Keyword → Text form
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15. Question Analysis
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16. Question Analysis
● Named entity recognition
● Type identification /Extract focus
● ESG/PAS
● Relationship detection
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17. Question Analysis
1) Extract focus
2) Map to LAT
3) Broad Type classification
4) Detect if special handling is
needed (e.g. nested question)
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18. Query Decomposition
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19. Query Decomposition
● Keyword identification
● LAT (Lexical Answer Type)
● IBM Pat. US20120078890 for
confidence estimation of LAT
● optimization objective: choosing
keywords out of nontrivial set of
words based on ML
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20. Query Decomposition
In 1894 C.W. Post created his warm cereal
drink Postum in this Michigan city
● Focus: this Michigan City
● LAT: Michigan
● Keywords: 1894, C.W. Post,
created, warm, cereal, drink,
Postum, Michigan, City
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21. Query Decomposition
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22. Primary Search
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23. Primary Search
● Lucene and Indri search engine
● Preprocessing generated
keyword->text based documents
● Keyword associated with found
document added to candidate
answer list
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24. Hypothesis generation
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25. Supporting Evidence Retrieval
Unlike most sea animals, in the sea horse this pair
of sense organs can move independently of one
another
Question decomposition:
Which [sense organ] of [Sea Horse] move independently?
Hypothesis generation:
A Sea Horse can move its eyes independently.
A Sea Horse can move its ears independently.
A Sea Horse can move its skin independently.
A Sea Horse can move its nose independently.
A Sea Horse can move its tung independently.
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26. http://angelalmassey.com/SHC/about.html
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27. Supporting Evidence
● Generated Candidate Answer is
● ESG'd
● PAS'd
● searched against corpus
● LATs used to determine whether
a candidate answer is an
instance of the answer types
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28. Supporting Evidence
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29. Scoring
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30. Scoring
● Optimization objective
(confidence estimation framework)
● Relational (PRISMATIC, Dbpedia)
● Taxonomic,Geospacial
● Temporal, Source Reliability
● Gender, Name consistency
● Passage Support
● 30 Theory consistency
© 2013 IBM Corporation

31. Scoring challenges
● Feature significance different for
● Different questions
● Different question classes
● Very heterogeneous features
● Normalization problem
● Missing features
● Class imbalance
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32. Merging and ranking
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33. Merging and ranking
1. John Fitzgerald Kennedy 2. Kennedy, 3. JFK
● Different Scores
● Merge to canonical form
● Morphological
● Pattern-based
● Table Lookup
● Partially generated from Wikipedia
disabiguation pages
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34. Example
MYTHING IN ACTION: One legend says this
was given by the Lady of the Lake & thrown
back in the lake on King Arthur’s death.
●Watson merged sword + Excalibur
to “sword” (canonical form)
● Preserved relation
● more_specific(sword)->Excalibur
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35. ML in Ranking
● Experiments with logistic regression, support
vector machines, linear and nonlinear
kernels, ranking SVM, boosting, single and
multilayer neural nets, decision trees, locally
weighted learning
● Finally:
regularized logistic regression
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36. Normalization
● Q set of all candidate answers
● Feature x_ij
● j feature, i answer
● missing values imputed
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37. Ranking
● Based on training set n > 10K
● IBM SPSS Modeler
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38. Evidence Sources
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39. Automatic Learning
● Read through text semantically
● Statistically rank annotated text
● generate new knowledge
● Inventors patent inventions 0.8
● officials submit resignations 0.7
● people earn degrees at schools 0.9
● fluid is a liquid 0.6
● liquid is a fluid 0.5
● vessels sink 0.7
● people sink 8-balls (0.5) (in pool/0.8)
39 © 2013 IBM Corporation

40. Next steps
● “Jeopardy!” - Watson was
● Open domain
● Large training set
● New “Watsons” are
● Closed domain
● Small, but growing training set
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41. Demo
● Bit.ly/go4bluemix
41 © 2013 IBM Corporation

42. References
[1] Jeffrey Kabot, “Deep Parsing”
[2] Richard Nordquist, “slot and
filler”
[3] The Journal of Research and
Development, Vol 56, 2012
42 © 2013 IBM Corporation