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Watson Systems. By- Team 7 : Pallav Dhobley 09005012 Vihang Gosavi 09005016 Ashish Yadav 09005018. Motivation:. Deep-Blue’s Triumph over Kasparov in 1997. In search of new challenge. Jeopardy!. 2004 – Search ends! One of the most popular Quiz show in U.S.A.

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Watson Systems

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Watson systems

Watson Systems


Team 7 :


VihangGosavi 09005016




  • Deep-Blue’s Triumph over Kasparov

    in 1997.

  • In search of

    new challenge.



  • 2004 – Search ends!

  • One of the most popular Quiz show in U.S.A.

  • Broad/Open Domain.

  • Complex Language.

  • High Speed.

  • High precision.

  • Accurate Confidence.



  • 2004 – Search ends!

  • One of the most popular Quiz show in U.S.A.

  • Broad/Open Domain.

  • Complex Language.

  • High Speed.

  • High precision.

  • Accurate Confidence.

*le IBM

Easier than playing chess

Easier than playing Chess?

  • Chess:

  • Finite moves and states.

  • Mathematically well defined

  • search space

  • Symbols have mathematical

  • meaning

  • Natural Language:

  • Implicit

  • Highly Contextual

  • Ambiguous

  • Imprecise

Easier than playing chess1

Easier than playing Chess?


  • Chess:

  • Finite moves and states.

  • Mathematically well defined

  • search space

  • Symbols have mathematical

  • meaning

  • Natural Language:

  • Implicit

  • Highly Contextual

  • Ambiguous

  • Imprecise

Easy question

Easy Question

(LN(1,25,46,798*π))^3 / 34,600.47



Easy question1

Easy Question:

(LN(1,25,46,798*π))^3 / 34,600.47



Hard question

Hard Question:

  • Where was our “father of nation” born?

    - contextual.

    - imprecise.

  • Easy for us Indians to relate term “father of nation” with M.K. Gandhi.

  • Not the same with computers.

  • Need of learning from As-Is content.

Learning the as is text nlp

Learning the As-Is text (NLP):

What is watson

What is Watson?

  • Advanced Search Engine? ×

  • Some fancy Database Retrieval System? ×

  • Beginning of Sky-Net?×

  • Science behind an Answer?√

Watson systems


Principles of deepqa

Principles of DeepQA:

  • Massive Parallelism

    - Each hypothesis and interpretation is analyzed independently in parallel to generate candidate answers.

  • Many experts

    -Facilitate the integration and contextual evaluation of a wide range of analytics generated by several algorithms running in parallel.

Principles of deepqa ctd

Principles of DeepQA (ctd.)

  • Pervasive Confidence Estimation

    - No component commits to an answer

  • Integrate shallow and deep knowledge

    -Using shallow and deep semantics for better precision


    Shallow semantics:Keyword matching

    Deep semantics:Logical Relationships

Shallow semantics

Shallow Semantics:

Deep semantics

Deep Semantics:

How does watson learn

How does Watson Learn?

Step 0 content acquisition

Step 0 : Content Acquisition

  • Identifying and gathering the

    content to be used for answering

    and evidence supporting.

  • Involves analyzing example questions from the problem space which consists of Q-A from previous games.

  • Encyclopedias, dictionaries, wiki pages etc. are use to make up the evidence sources.

  • Extract , verify and merge the most informative nuggets as a part of content acquisition.

Step 1 question analysis

Step 1 : Question Analysis

The initial analysis that determines

how the question will be processed

by the rest of the system.

  • Question Classification e.g. puzzle/math

  • Focus and (Lexical Answer Type)LAT e.g. “On this day” LAT – date/day

  • Relation Detection e.g. sea(India, x, west)

  • Decomposition - divide and conquer.

Step 2 hypothesis generation

Step 2 : Hypothesis Generation

  • Primary search :

    • Keyword based search

    • Top 250 results are considered for Candidate Answer generation.

    • Empirical statistics : 85% time answer is within top 250 results.

  • CA generation : above results are further processed for CA generation.

  • Soft Filtering

    • It reduces set of candidate answers using superficial analysis (machine learning).

    • Reduction in number of CA to approx. 100

    • Answers are not fully discarded , may be reconsidered at final stage.

Step 2 hypothesis generation ctd

Step 2: Hypothesis Generation (ctd.)

4.Each CA plugged back into the question is considered a hypothesis which the system has to prove correct with some threshold of confidence.

5.If failed at this state , system has no hope of answering the question whatsoever.

  • Noise tolerance.

Step 3 hypothesis evidence scoring

Step 3 : Hypothesis & evidence scoring

  • Evidence retrieval :

    • Further evidences are gathered to support the Hypothesis formed in last step .

      e.g. Passage search: gathering passages by adding CA to primary search query.

  • Scoring:

    • Deep content analysis

    • Determines degree of certainty that retrieved evidence supports the CA.

Step 4 final merging and ranking

Step 4 : Final Merging and Ranking

  • Merging:

    • Merging all the hypothesis which give you the same answer.

    • Using an ensemble of matching, normalization and co-reference resolution algorithms, Watson identifies equivalent and related hypothesis.

  • Ranking and confidence estimation:

    • The final set of hypothesis after merging are ran over set of training questions with known answers.


Example :

  • Q : “Who is the antagonist of Stevenson's Treasure Island?”

  • Step 1 :

    Parse and generate a logical structure to describe the question.


    -antagonist_of(X, Stevenson’s TI)

    -adj_possesive(Stevenson, TI)

Example ctd

Example (ctd.):

  • Step 2:

    Generating semantic assumptions

    - island (TI) -book(TI) - movie(TI)

    -author(Stevenson) -director(Stevenson)

  • Step 3:Builds different semantic queries based on phrases, keywords and semantic assumptions.

  • Step 4 : Generates 100s of answers based on passages, documents and facts returned from 3.

    Long-John Silver is likely to be one of them.

Example ctd1

Example (ctd.):

  • Step 5:Formulate evidence in support or refutation.

    (+VE) evidence :

    1. Long-John Silver the main character in TI.

    2. The antagonist in Treasure Island is Long-John Silver

    3. Treasure Island, by Stevenson was a great book.

    (-VE) evidence :

    Stevenson = Richard Lewis Stevenson

    antagonist = Wolverine

Example ctd2

Example (ctd.):

  • Step 6:

    -Combine all the evidence and their scores.

    -Analyze evidences to compute confidence and return the most confident answer.

    Long-John Silver in this case !

Watson performance

Watson- Performance:

Watson s brain software

Watson’s Brain (Software):

  • Languages used : Java , C++ , prolog.

  • Apache Hadoop framework for distributed computing.

  • Apache UIMA framework.

    • Helps in DeepQA’s demand for Massive Parallelism.

    • Facilitated rapid component integration, testing , evaluation

  • SUSE Linux Enterprise Server 11

Watson s brain hardware

Watson’s Brain(Hardware):

  • One Jeopardy! Question takes 2hours on normal desktop computer!

  • The real task

    - Confidence determination before buzzing.

  • High Time need of faster Hardware support.

Watson s brain ctd

Watson’s Brain: (ctd.)

  • Total Ninety POWER-750 servers.

  • Total 2880 POWER7 processor cores.

  • Total 16 Terabytes of R.A.M.

  • Each POWER-750 server uses a 3.5 GHz POWER7eight core processor, with 4 Threads per core.

  • Size of total 8 refrigerators.

  • Can process data up-to the speed of 500 GB/s.

Watson s brain ctd1

Watson’s Brain: (ctd.)

Watson runtime stack

Watson – Runtime Stack

The final blow

The Final Blow!

  • 3 rounds of Jeopardy! Between Watson , Rutter& Jennings.

  • Watson comprehensively defeats it’s competitors with net score of $77,147

  • Jennings managed $24,000.

  • Rutter ended third with $21,600.

The final blow ctd

The Final Blow! (ctd.)

“I for one welcome our new computer overlords” - Jennings



  • High performance analytics

  • Non-cognitive

  • Smart Learner

  • Not invincible

Watson suits

Watson & Suits

  • Tech support

  • Knowledge management

  • Business Intelligence

  • Improvised Information sharing

Watson for society health care

Watson for society- Health Care

  • Symptoms

  • Patient Records

  • Tests

  • Medications

  • Notes/Hypothesis

  • Texts, Journals

Diagnosis Models

Finding appropriate

“Disease” , As per

Asked by adjoining

“Symptoms” and




  • Watson Systems:


  • Wiki Page


  • Research Papers:




  • Jeopardy! IBM Watson Day 1 (Feb 14, 2011)


  • Science Behind an Answer-


  • The AI magzine




  • Philip Resnik. 1999.Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research.

  • Tom M. Mitchell. 1997. Machine Learning. Computer Science Series. McGraw-Hill.

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