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Carol Beer (Little Britain)

Carol Beer (Little Britain). Computer says “no”. Question Answering. Lecture 1 (Today): Introduction; History of QA; Architecture of a QA system; Evaluation. Lecture 2 (Friday): Question Classification; NLP techniques for question analysis; POS-tagging; Parsing; Semantic analysis; WordNet.

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Carol Beer (Little Britain)

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  1. Carol Beer (Little Britain)

  2. Computer says “no”

  3. Question Answering • Lecture 1 (Today):Introduction; History of QA; Architecture of a QA system; Evaluation. • Lecture 2 (Friday):Question Classification; NLP techniques for question analysis; POS-tagging; Parsing; Semantic analysis; WordNet. • Lecture 3 (Next Monday):Retrieving Answers; Document pre-processing; Tokenisation; Stemming; Lemmatisation; Named Entity Recognition; Anaphora Resolution; Matching; Use of knowledge resources; Reranking; Sanity checking.

  4. What is Question Answering? ?

  5. Information Pinpointing Information required: Average number of car accidents per year in Sweden. Two ways of getting this information: • Ask Google or a similar search engine (good luck!) • Ask a QA system the question:What’s the rate of car accidents in Sweden?

  6. QA vs IR • Traditional method for information access: IR (Information Retrieval) • Think of IR as finding the “right book in a library” • Think of QA as a “librarian giving you the book and opening it on the page with the information you’re looking for”

  7. QA vs IE • Traditional method for information access: IE (Information Extraction) • Think of IE as finding answers to a pre-defined question (i.e., a template) • Think of QA as asking any question you like

  8. What is Question Answering? • Questions in natural language, not queries! • Answers, not documents!

  9. Why do we need QA? • Information overload problem • Accessing information using traditional methods such as IR and IE are limited • QA increasingly important because: • Size of available information grows • There is duplicate information • There is false information • More and more “computer illiterates” accessing electronically stored information

  10. Information Avalanche • Available information is growing*: • 1999: 250MB pp for each person on earth • 2002: 800MB pp for each person on earth • People want specific information * source: M.de Rijke 2005

  11. People ask Questions* * source: M.de Rijke 2005

  12. Why is QA hard? (1/3) • Questions are expressed in natural language (such as English or Italian) • Unlike formal languages, natural languages allow a great deal of flexibility • Example: • What is the population of Rome? • How many people live in Rome? • What’s the size of Rome? • How many inhabitants does Rome have?

  13. Why is QA hard? (2/3) • Answers are expressed in natural language (such as English or Italian) • Unlike formal languages, natural languages allow a great deal of flexibility • Example: …is estimated at 2.5 million residents… … current population of Rome is 2817000… …Rome housed over 1 million inhabitants…

  14. Why is QA hard? (3/3) • Answers could be spread across different documents • Examples: • Which European countries produce wine?[Document A contains information about Italy, and document B about France] • What does Bill Clinton’s wife do for a living?[Document A explains that Bill Clinton’s wife is Hillary Clinton, and Document B tells us that she’s a politician]

  15. History of QA (de Rijke & Webber 2003) • QA is by no means a new area! • Simmons (1965) reviews 15 implemented and working systems • Many ingredients of today’s QA systems are rooted in these early approaches • Database oriented systems, domain independent, as opposed to today’s systems that work on large sets of unstructured texts

  16. Examples of early QA systems • BASEBALL (Green et al. 1963)Answers English questions about scores, locations and dates of baseball games • LUNAR (Woods 1977)Accesses chemical data on lunar material compiled during the Apollo missions • PHLIQA1 (Scha et al. 1980)Answers short questions against a database of computer installations in Europe

  17. Recent work in QA • Since the 1990s research in QA has by and large focused on open-domain applications • Recently interest in restricted-domain QA has increased, in particular in commercial applications • Banking, entertainment, etc.

  18. Architecture of a QA system corpus query IR Question Analysis question documents/passages answer-type Document Analysis question representation passage representation Answer Extraction answers

  19. Question Analysis • Input:Natural Language Question • Output:Expected Answer Type(Formal) Representation of Question • Techniques used:Machine learning, parsing

  20. Document Analysis • Input:Documents or Passages • Output:(Formal) Representation of Passages that might contain the answer • Techniques used:Tokenisation, Named Entity Recognition, Parsing

  21. Answer Retrieval • Input:Expected Answer TypeQuestion (formal representation)Passages (formal representation) • Output:Ranked list of answers • Techniques used:Matching, Re-ranking, Validation

  22. Example Run corpus query IR Question Analysis question documents/passages answer-type Document Analysis question representation passage representation Answer Extraction answers

  23. Example Run How long is the river Thames? corpus query IR Question Analysis question documents/passages answer-type Document Analysis question representation passage representation Answer Extraction answers

  24. Example Run length river thames corpus query IR Question Analysis question documents/passages answer-type Document Analysis question representation passage representation Answer Extraction answers

  25. Example Run corpus MEASURE query IR Question Analysis question documents/passages answer-type Document Analysis question representation passage representation Answer Extraction answers

  26. Example Run corpus query IR Question Analysis question Answer(x) & length(y,x) & river(y) & named(y,thames) documents/passages answer-type Document Analysis question representation passage representation Answer Extraction answers

  27. Example Run A: NYT199802-31 B: APW199805-12 C: NYT200011-07 corpus query IR Question Analysis question documents/passages answer-type Document Analysis question representation passage representation Answer Extraction answers

  28. Example Run A: 30(u) & mile(u) & length(v,u) & river(y) B: 60(z) & centimeter(z) & height(v,z) & dog(z) C: 230(u) & kilometer(u) & length(x,u) & river(x) corpus query IR Question Analysis question documents/passages answer-type Document Analysis question representation passage representation Answer Extraction answers

  29. Example Run corpus query IR Question Analysis question documents/passages C: 230 kilometer A: 30 milesB: 60 centimeter answer-type Document Analysis question representation passage representation Answer Extraction answers

  30. Evaluating QA systems • International evaluation campaigns for QA systems (open domain QA): • TREC (Text Retrieval Conference)http://trec.nist.gov/ • CLEF (Cross Language Evaluation Forum)http://clef-qa.itc.it/ • NTCIR (NII Test Collection for IR Systems)http://www.slt.atr.jp/CLQA/

  31. TREC-QA (organised by NIST) • Annual event, started in 1999 • Difficulty of the QA task increased over the years: • 1999: Answers in snippets, ranked list of answers; • 2005: Exact answers, only one answer. • Three types of questions: • Factoid questions • List questions • Definition questions

  32. QA@CLEF • CLEF is the “European edition” of TREC • Monolingual (non-English) QA • Bulgarian (BG), German (DE), Spanish (ES), Finnish (FI), French (FR), Italian (IT), Dutch (NL), Portuguese (PT) • Cross-Lingual QA • Questions posed in source language, answer searched in documents of target language • All combinations possible

  33. Open-Domain QA • QA at TREC is considered “Open-Domain” QA • Document collection is Acquint Corpus(over a million documents) • Questions can be about anything • Restricted-Domain QA • Documents described a specific domain • Detailed questions • Less redundancy of answers!

  34. TREC-type questions • Factoid questions • Where is the Taj Mahal? • List questions • What actors have played Tevye in `Fiddler on the Roof'? • Definition/biographical questions • What is a golden parachute? • Who is Vlad the Impaler?

  35. What is a correct answer? • Example Factoid Question • When did Franz Kafka die? • Possible Answers: • Kafka died in 1923. • Kafka died in 1924. • Kafka died on June 3, 1924 from complications related to Tuberculosis. • Ernest Watz was born June3,1924. • Kafka died on June 3, 1924.

  36. What is a correct answer? • Example Factoid Question • When did Franz Kafka die? • Possible Answers: • Kafka died in 1923. • Kafka died in 1924. • Kafka died on June 3, 1924 from complications related to Tuberculosis. • Ernest Watz was born June3,1924. • Kafka died on June 3, 1924. Incorrect

  37. What is a correct answer? • Example Factoid Question • When did Franz Kafka die? • Possible Answers: • Kafka died in 1923. • Kafka died in 1924. • Kafka died on June 3, 1924 from complications related to Tuberculosis. • Ernest Watz was born June3,1924. • Kafka died on June 3, 1924. Inexact (under-informative)

  38. What is a correct answer? • Example Question • When did Franz Kafka die? • Possible Answers: • Kafka died in 1923. • Kafka died in 1924. • Kafka died on June 3, 1924 from complications related to Tuberculosis. • Ernest Watz was born June3,1924. • Kafka died on June 3, 1924. Inexact (over-informative)

  39. What is a correct answer? • Example Question • When did Franz Kafka die? • Possible Answers: • Kafka died in 1923. • Kafka died in 1924. • Kafka died on June 3, 1924 from complications related to Tuberculosis. • Ernest Watz was born June3,1924. • Kafka died on June 3, 1924. Unsupported

  40. What is a correct answer? • Example Question • When did Franz Kafka die? • Possible Answers: • Kafka died in 1923. • Kafka died in 1924. • Kafka died on June 3, 1924 from complications related to Tuberculosis. • Ernest Watz was born June3,1924. • Kafka died on June 3, 1924. Correct

  41. Answer Accuracy # correct answers Answer Accuracy = --------------------------- # questions

  42. Correct answers to list questions System A: France Italy Example List Question Which European countries produce wine? System B: Scotland France Germany Italy Spain Iceland Greece the Netherlands Japan Turkey Estonia

  43. Evaluation metrics for list questions • Precision (P): # answers judged correct & distinct P = ---------------------------------------------- # answers returned • Recall (R): # answers judged correct & distinct R = ------------------------------------------------ # total answers • F-Score (F): 2*P*R F = ------------ P+R

  44. Correct answers to list questions System A: France Italy Example List Question Which European countries produce wine? System B: Scotland France Germany Italy Spain Iceland Greece the Netherlands Japan Turkey Estonia P = 1.00 R = 0.25 F = 0.40 P = 0.64 R = 0.88 F = 0.74

  45. Other evaluation metrics System A: Ranked answers (Accuracy = 0.2) System B: Ranked answers (Accuracy = 0.1)

  46. Mean Reciprocal Rank (MRR) • Score for an individual question: • The reciprocal of the rank at which the first correct answer is returned • 0 if no correct response is returned • The score for a run: • Mean over the set of questions in the test

  47. MRR in action System A: MRR = (.2+1+1+.2)/10 = 0.24 System B: MRR = (.5+.33+.5+.25+1+.5+.5+.5)/10=0.42

  48. Open-Domain Question Answering • TREC QA Track • Factoid questions • List questions • Definition questions • State-of-the-Art • Hard problem • Only few systems withgood results

  49. Friday • QA Lecture 2: • Question Classification • NLP techniques for question analysis • POS-tagging • Parsing • Semantic analysis • Use of lexical resources such as WordNet

  50. Question Classification (preview) • How many islands does Italy have? • When did Inter win the Scudetto? • What are the colours of the Lithuanian flag? • Where is St. Andrews located? • Why does oil float in water? • How did Frank Zappa die? • Name the Baltic countries. • Which seabird was declared extinct in the 1840s? • Who is Noam Chomsky? • List names of Russian composers. • Edison is the inventor of what? • How far is the moon from the sun? • What is the distance from New York to Boston? • How many planets are there? • What is the exchange rate of the Euro to the Dollar? • What does SPQR stand for? • What is the nickname of Totti? • What does the Scottish word “bonnie” mean? • Who wrote the song “Paranoid Android”?

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