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WATSON @ RPI

WATSON @ RPI

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WATSON @ RPI

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  1. WATSON @ RPI Watson Technologies and Open Architecture Question Answering Professor Jim Hendler Simon Ellis Kate McGuire Nicole Negedly Avi Weinstock Matt Klawonn Jenn Chan Sarabeth Jaffe

  2. Introduction

  3. IBM Watson

  4. … a piece of software that will run on your laptop Though very slowly Specialised hardware and control platform … an implementation of the DeepQA concept … the first iteration of the ‘cognitive computing’ platform … a very clever artificial intelligence Avery clever application of human intelligence Watson is…

  5. Inside Watson Watson pipeline as published by IBM; see IBM J Res & Dev56 (3/4), May/July 2012, p. 15:2

  6. Question Analysis Nicole Negedly

  7. Question Analysis

  8. 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? Question Analysis Focus Terms: “Who”, “president of Rensselaer Polytechnic Institute” Answer Types: Person, President Who is the president of Rensselaer Polytechnic Institute?

  9. Parsing and semantic analysis • What information about a previously unseen piece of English text can Watson determine? • How is this information useful?

  10. Parsing • Stanford’s NLP toolset is used

  11. Semantic relations in WordNet • Princeton University’s WordNet • Words are grouped into groups of synonyms called synsets • Relationships exist between noun synsets • hypernym/hyponym: type-of relation • e.g. Canine is a hypernym of dog • holonym/meronym: part-of relation • e.g. Building is a holonym of window

  12. How is this useful? • This information can be used to “understand” a question • Current Question Analysis work with RPI’s version of Watson • Creating and training machine learning classifiers Parse Trees Dependency Relations Coreferences Named Entities Semantic Relations Manually Annotated Questions Classifiers New Question Critical Elements of Question

  13. Question analysis pipeline Structured Annotations of Question: Focus, answer types, Useful search queries Unstructured Question Text Parsing & Semantic Analysis Machine Learning Classifiers

  14. Candidate Generation Kate McGuire

  15. Search Result Processing and Candidate Generation

  16. Primary Search • Primary Search is used to generate our 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 search engine which returns a set number of highly relevant documents and their ranks.

  17. Search Result Processing • Search Result Processing restructures the information in the document so it is useful. • HTML tags are cleaned from the document • Passage Retrieval/Chunking • Breaks the document down into smaller pieces • Adds information, such as the html text, length, place in the document, etc. • Passage Parsing • Parse trees are formed for each passage

  18. 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.

  19. Search Result Processing andCandidate Generation

  20. Scoring & Ranking Matt Klawonn

  21. Scoring & Ranking

  22. Scoring • Analyzes how well a candidate answer relates to the question • Two basic types of scoring algorithm • Context-independent scoring • Context-dependent scoring

  23. Types of scorers • Context-independent • Question Analysis • Ontologies (DBpedia, YAGO, etc) • Reasoning • Context-dependent • Analyzes natural language that candidates appear in • Relies on “passages” found during search

  24. Scorers • Examples of scorers include • Passage Term Match • Textual Alignment • Skip-Bigram • Each of these scores supportive evidence • Scores are then merged to produce a single candidate score

  25. Inside Watson Watson pipeline as published by IBM; see IBM J Res & Dev56 (3/4), May/July 2012, p. 15:2

  26. The Tao of UIMA Simon Ellis

  27. UIMA • ‘Unstructured Information Management Architecture’ • A platform for the analysis of unstructured information and its integration with search technologies • Permits multi-modal analysis of collections or archives

  28. UIMA http://uima.apache.org/d/uimaj-2.4.0/

  29. ‘Unstructured information’ • The most rapidly-growing source of information in existence • The internet • Print media • Video recordings • Audio recordings • ... • “Unstructured information is just information that doesn’t have the kind of structure you need it to have for what you’re doing.” [Peter Fox, X-Informatics class]

  30. UIMA (again) • The UIMA platform can be thought of in four ways: • A specification for component interfaces for, and in, an analytics pipeline • A specification of certain design patterns for that pipeline • An outline of 2 data representations: in-memory annotations for local analysis and XML representation for remote web integration • An outline for possible development roles allowing tools to be used by users with a wide range of skills

  31. CAS • Common Analysis Structure (CAS) • Object-based structure • Allows representation of objects, properties and values • Stores arbitrary data structures • Annotations • Types • Object types may be related by single-inheritance • Contains document being analysed, either physically or logically • Results of analysis are shared and recorded in a CAS

  32. Annotator • Core UIMA component type • Contains analysis algorithms designed to work on data contained in a CAS • Original document • Annotation • Search evidence • Candidate score • ... • Form the building blocks of Analysis Engines

  33. Analysis Engine • Building blocks of a UIMA pipeline • Section of code containing 1 or more annotators • Analyses source document(s) and provides analysis results • Results typically represent metadata about the source • Analysis Engines are effectively software agents that discover and record metadata

  34. Example http://uima.apache.org/d/uimaj-2.4.0/

  35. Sofas and CAS Views • Sofa • Subject of Analysis • A piece of data intended for analysis by UIMA components • CAS View • A section of a CAS dedicated to one Sofa • Shares the same name as its Sofa • May be dynamically created as needed by applications or AEs • Each Sofa permits a different perspective of an artefact

  36. Example Dr Shirley Ann Jackson Chairman, USNRC Teacher of physics President, RPI Researcher at Bell Labs IBM Board of Directors

  37. Descriptors • All components consist of two parts • Code • Descriptor (declaration) • Functions of the descriptor • Contains metadata about the code block • Name • Structure • Behaviour • Used in component discovery, reuse, and tool composition

  38. UIMA (again, again) • Highly reliant on XML • Flexible • Extensible • XML... • ... describes components and their behaviour • ... controls data (CAS) flow through the pipeline • ... is used to create larger components from subcomponents • Aggregate Analysis Engines

  39. Aggregate Analysis Engine • A complex analysis engine made up of other components • May contain simple AEs or other AAEs • Components further down the pipeline may rely on all output • Performs a larger, complete task, e.g. named entity recognition • language detection and tokenisation • part-of-speech detection • deep grammatical parsing • named entity recognition

  40. CAS Multiplier • Creates 0 or more new CAS objects from an input CAS • May be used to duplicate or merge CAS objects • e.g.... • ... creating alternative versions of an input Sofa • ... breaking a large input CAS into multiple smaller pieces • ... aggregating multiple input CAS into a single output

  41. Inside Watson Watson pipeline as published by IBM; see IBM J Res & Dev56 (3/4), May/July 2012, p. 15:2

  42. UIMA, once more • UIMA runs in the Java Runtime Environment • Uses XML code to run system • UIMA framework reads XML dynamically and creates objects using them • Only the UIMA framework itself is compiled SO HOW DOES IT WORK?

  43. How it works • Abstract class prototyping • UIMA Framework objects are usually derived from a base class • Function signature • UIMA Framework objects each have certain functions which can or must be overridden • initialize() • process() • This ensures all classes are of known supertypes and have a recognisable function signature for all key functions

  44. How it works • Reflection • The ability of a computer program to examine and modify the structure and behavior (specifically the values, meta-data, properties and functions) of an object at runtime. • XML descriptors define the nature of objects • class name • constructor parameters • ... • UIMA dynamically creates objects using reflection

  45. The ‘magic code’ // create type of obj we want JCasAnnotatorann = null; // use Java inbuilt function to create abstract class Class annClass = Class.forName("com.ibm.tutorial.tycor"); // get constructors for abstract class type Constructor cons = annClass.getConstructor(<params>); // should return a JCasAnnotator object ann= cons.newInstance(<params>);

  46. UIMA, finally • Effectively an interpreter for code ‘scripted’ in XML and Java • Component-oriented design makes scaling easy • BlueJ (Jeopardy! hardware) had ≫ 2,000 cores • Most easily written in Java • Java runs in the Java Runtime Environment • Dynamic typing & reflection are therefore possible • Could not have been written in C++08 • An OS for multimodal, unstructured information management

  47. Questions & Answers