Why Watson Won: A cognitive perspective
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The document discusses IBM Watson's capabilities in natural language processing and its methods of answering questions through cognitive architecture. It outlines how Watson analyzes questions, generates candidate answers, and scores them for accuracy, employing techniques such as semantic analysis and machine learning. The authors explore Watson's performance as a cognitive model and its potential role in more complex cognitive systems.
Why Watson Won: A cognitive perspective
- 1. Tetherless World Constellation Why Watson Won: A cognitive perspective Jim Hendler and Simon Ellis Tetherless World Professor of Computer,Web and Cognitive Sciences Director, Rensselaer Institute for Data Exploration and Applications Rensselaer Polytechnic Institute (RPI) http://www.cs.rpi.edu/~hendler @jahendler (twitter)
- 2. IBM Watson Tetherless World Constellation
- 3. How’d I get into it? Watson and Semantic Web Tetherless World Constellation IBM
- 4. Watson and Semantic Web Tetherless World Constellation IBM
- 5. Is Watson cognitive? ??? “The computer‟s techniques for unraveling Jeopardy! clues sounded just like mine. That machine zeroes in on key words in a clue, then combs its memory (in Watson‟s case, a 15-terabyte data bank of human knowledge) for clusters of associations with those words. It rigorously checks the top hits against all the contextual information it can muster: the category name; the kind of answer being sought; the time, place, and gender hinted at in the clue; and so on. And when it feels „sure‟ enough, it decides to buzz. This is all an instant, intuitive process for a human Jeopardy! player, but I felt convinced that under the hood my brain was doing more or less the same thing.” — Ken Jennings
- 6. Outline Tetherless World Constellation • Is Ken right? – How Watson Works – Watson as a cognitive architecture?? – Beyond Watson
- 7. Inside Watson ??? Watson pipeline as published by IBM; see IBM J Res & Dev 56 (3/4), May/July 2012, p.
- 9. ??? Question analysis What is the question asking for? Which terms in the question refer to the answer? Given any natural language question, how can Watson accurately discover this information? Who is the president of Rensselaer Polytechnic Institute? Question Analysis Focus Terms: “Who”, “president of Rensselaer Polytechnic Institute” Answer Types: Person, President
- 10. Parsing and semantic analysis What information about a previously unseen piece of English text can Watson determine? ??? How is this information useful? Natural Language Parsing Semantic Analysis - grammatical structure - meanings of words, phrases, etc. - parts of speech - synonyms, entailment - relationships between words - hypernyms, hyponyms - ...etc. - ...etc.
- 11. Question analysis pipeline Unstructured Question Text Parsing & Semantic Analysis Structured Annotations of Question: Focus, answer types, Useful search queries Machine Learning Classifiers ???
- 12. Search Result Processing and Candidate ??? Generation
- 13. Primary Search ??? Primary Search is used to generate the corpus of information from which to take candidate answers, passages, supporting evidence, and essentially all textual input to the system It formulates queries based on the results of Question Analysis These queries are passed into a (cached) search engine which returns a set number of highly relevant documents and their ranks. on the open Web this could be a regular search engine (our
- 14. Candidate Generation ??? Candidate Generation generates a wide net of possible answers for the question from each document. Using each document, and the passages created by Search Result Processing, we generate candidates using three techniques: Title of Document (T.O.D.): Adds the title of the document as a candidate. Wikipedia Title Candidate Generation: Adds any noun phrases within the document‟s passage texts that are also the titles of Wikipedia articles. Anchor Text Candidate Generation: Adds candidates based on the hyperlinks and metadata within the document.
- 15. Search Result Processing and Candidate Generation ???
- 17. Scoring Analyzes how well a candidate answer relates to the question Two basic types of scoring algorithm Context-independent scoring Context-dependent scoring ???
- 18. Types of scorers Context-independent Question Analysis Ontologies (DBpedia, YAGO, etc) ??? Type hierarchy reasoning Context-dependent Analyzes feature of the natural language environment where candidates were found Relies on “passages” found during search Many special purpose ones used in Jeopardy
- 19. Scorers Passage Term Match Textual Alignment ??? Skip-Bigram Each of these scores supportive evidence These scores are then merged to produce a single candidate score
- 20. Example:Textual Alignment ??? Finds an optimal alignment of a question and a passage Assigns “partial credit” for close matches “Who is the President of RPI?” Who President of RPI. Shirley Ann Jackson is the President of RPI.
- 21. Skip-Bigram Constructs a graph Nodes represent terms (syntactic objects) ??? Edges represent relations Extracts skip-bigrams A skip-bigram is a pair of nodes either directly connected or which have only one intermediate node Skip-bigrams represent close relationships between terms Scores based on number of common skip-bigrams
- 22. Example Who authored “The Good Earth”? “Pearl Buck, author of the good earth…” ???
- 23. Watson Summary Tetherless World Constellation • Watson works by – Analyzing the question • natural language parsing • text extraction – Generating a large number of candidates • mostly search heuristics – Scoring each • through multiple scorers • with weights adjusted by learning algorithm – Returning top candidate
- 24. MiniDeepQA (Not Watson!) ??? RPI students implementing a DeepQA pipeline to explore the principles underlying this kind of Q/A system (THIS IS NOT WATSON!) Pipeline development Data caching Graphical and command line interfaces Parsing Scoring
- 29. ??? Examples had to get this one right!
- 30. Scoring ??? One of DeepQA‟s main strengths is aggregating a number of different scoring algorithms capable of running in parallel. RPI scorers are primitive compared to IBM‟s, but allow us to explore the principles allow us to explore different algorithms for computing scores allow us to create new ones not tried by IBM
- 31. Scoring Principles: combine evidence He was the Prime Minister of Canada in 1993. candidates could include Trudeau, Harper, Campbell, Chretien, Mulroney… Try (Research): Trudeau was Prime Minister of Canada in 1993 (doesn‟t match) Campbell was Prime Minister of Canada in 1993 (MATCH) Chretien was Prime Minister of Canada in 1993 (MATCH) Scoring Research & type match Trudeau: Re-search NO; Type: Yes Campbell: Re-search YES; Type: No Chretien: Re-search YES; Type: Yes WHO WAS CHRETIEN? ???
- 32. New Scoring types ??? We can explore how new kinds of information can be added to the Watson scoring pipeline Example: new NLP extraction techniques Example: Specialized Web Sources Adding a ML-based extractor built by Heng Ji Database advisor project Example: More complex inferencing Jeopardy questions are unambiguous, real world questions aren‟t • • Where is Montreal? Who is Jim Hendler? Example: Special purpose reasoning…
- 33. Special purpose reasoning ??? • Can we match simulation (or steer) large scale simulations to help answer NL questions? - eg. Answer questions such as “Why” and “How” integrated with large scale simulations
- 34. Alternate Universe Reasoning (Contexts) How can a Watson reasoner appropriately use Q/A contexts? Where was Yoda born? Very little is known about Yoda's early life. He was from a remote planet, but which one remains a mystery. Where was Yoda made? designed and built by Stuart Freeborn The Yoda puppet was originally for LucasFilm and Industrial Light & Magic. Where did Yoda live? Jedi Master Yoda went into voluntary exile on Dagobah Where did Yoda live in the Phantom Menace? ???
- 35. But back to the original question Tetherless World Constellation • Q: How does Watson fare as a cognitive model? • A: Poorly – no conversational ability – no concept of self – no deeper reasoning … • Q: How does Watson fare as a model of question answering?
- 36. Watson and Q/A Tetherless World Constellation • Watson’s feed-forward pipeline has the following properties – lots of candidates generated • the more the better – “ad hoc” filtering pipelines • domain independent usually score lower than domain dependent – no “counter-reasoning” between answers • separately scored, only comparison is numbers
- 37. Production rules, modules, etc Tetherless World Constellation Production Rule style Architectures cf ACT-R (Anderson 1974; …2012) - modularization, but not Watson style - parallelization, but in rule productions (procedural memory) - declarative memory is fact based Watson is not well correlated, except for using search for declarative memory
- 38. Network based Tetherless World Constellation Network based architectures (cf. spreading activation (Collins 75), marker-passing (Hendler 86) … Microsaint 2006) - positive activations - inhibitory nodes (or other negative enforcers) Watson has no negative inhibition, does use network-based scorers
- 39. MAC/FAC Tetherless World Constellation MAC/FAC (Gentner & Forbus, 1991) Many are chosen, few are called model of analogic reasoning Strong correspondence in performance, not in mechanism New work by Forbus (SME) uses a more feed-forward mechanism (Discussions in progress)
- 40. Cognitive Architecture? Watson as “component” Decision Making Memory Reasoning Watson, Cogito, and Clarion Office of Research
- 41. Summary Tetherless World Constellation • Watson won by a combination of – – – – – natural language processing search technologies semantic typing (minimal reasoning) scoring heuristics machine learning (scorer tuning) • Watson Q/A has some interesting analogies to cognitive architectures of the past – but mainly at a “level of abstraction” • Watson as a memory component in a more complex cognitive system is a very intriguing possibility