1. CSC 9010 Spring 2011. Paula Matuszek A Brief Overview of Watson

2. 2 CSC 9010 Spring 2011. Paula Matuszek Watson QA system developed by IBM and collaborators “massively parallel probabilistic evidence-based architecture” Hardware is a high-end IBM system, the IBM Power7 platform. –10 Power7 server blades –90 servers –4 processors/server –8 cores/processor Robotic arm to press the buzzer. Input is text only, no speech recognition, no visual.

3. 3 CSC 9010 Spring 2011. Paula Matuszek Watson Software is built on top of UIMA: unstructured information management application. UIMA is a framework build by IBM and since open-sourced. The information corpus was downloaded and indexed offline; no web access during the game. Corpus was developed from a large variety of text sources: –baseline from wikipedia, Project Gutenberg, newspaper articles, thesauri, etc. –extend with web retrieval, extract potentially relevant text “nuggets”, score for informative, merge best into corpus Primary corpus is unstructured text, not semantically tagged or formal knowledge base. About 2% of Jeopardy! answers can be looked up directly. Also leverages semistructured and structured sources such as Wordnet and Yago.

4. 4 CSC 9010 Spring 2011. Paula Matuszek Components of DeepQA About 100 different techniques overall. Content acquisition: corpus, sample games. Offline, before game itself. Preprocessing Natural Language Tools Retrieve possible answers Score answers Buzz in Game strategies

5. 5 CSC 9010 Spring 2011. Paula Matuszek Preprocessing Determine question category –factoid –decomposable –puzzle –Note: excluded questions with AV components and “special instruction” categories Determine lexical answer type (LAT) –film? person? place? novel? song? –about 2500 in sample of 20,000 questions. About 12% of clues do not indicate type

6. 6 CSC 9010 Spring 2011. Paula Matuszek Initial Natural Language Processing Parse question Semantically tag the components of the question Reference or coreference resolution Named entity recognition Relation detection Decomposition into subqueries

7. 7 CSC 9010 Spring 2011. Paula Matuszek Retrieve Relevant Text Component most similar to a web search Focus is on recall Search engines include Indri, Lucene, SPARQL For some “Closed” LATs (All US States, presidents, etc) can generate candidate list directly Otherwise extract actual answer –title? –person? etc Several hundred hypotheses typically generated

8. 8 CSC 9010 Spring 2011. Paula Matuszek Score Hypotheses Evaluate candidate answers –soft filtering. Fast light-weight filters prune answers to about 100 –evidence retrieval. Additional structured or unstructured queries Score answers –LOTS of algorithms! -- More than 50 components –Range from simple word counts to complex spatial and temporal reasoning –Creates an evidence profile: taxonomic, geospatial, temporal, source reliability, etc Merge answers Determine ranking and confidence estimation

9. 9 CSC 9010 Spring 2011. Paula Matuszek And Game Strategies! Picking a category –tries to find the daily double –goes for lower cost categories to help learn the category When to bet? –Normally will buzz in if >50% certain –Will buzz in lower if only way to win –Will not buzz in if can’t lose except with a mistake How much to bet?

10. 10 CSC 9010 Spring 2011. Paula Matuszek References A clip of the end of the Jeopardy! game http://www.youtube.com/watch?v=8W36OuMU 0yE http://www.youtube.com/watch?v=8W36OuMU 0yE A good high-level overview. theswimmingsubmarine.blogspot.com/2011/02/ how-ibms-deep-question-answering.htmlA good high-level overview. theswimmingsubmarine.blogspot.com/2011/02/ how-ibms-deep-question-answering.html A detailed description: www.stanford.edu/class/cs124/AIMagzine- DeepQA.pdf www.stanford.edu/class/cs124/AIMagzine- DeepQA.pdf Many clips, blogs, and links: ibmwatson.com