Question Answering

1. Question Answering • Lecture 1 (two weeks ago):Introduction; History of QA; Architecture of a QA system; Evaluation. • Lecture 2 (last week):Question Classification; NLP techniques for question analysis; Tokenisation; Lemmatisation; POS-tagging; Parsing; WordNet. • Lecture 3 (today):Named Entity Recognition; Anaphora Resolution; Matching; Reranking; Answer Validation.

2. The Panda

3. A panda… A panda walks into a cafe. He orders a sandwich, eats it, then draws a gun and fires two shots in the air.

4. A panda… “Why?” asks the confused waiter, as the panda makes towards the exit. The panda produces a dictionary and tosses it over his shoulder. “I am a panda,” he says. “Look it up.”

5. The panda’s dictionary Panda. Large black-and-white bear-like mammal, native to China. Eats, shoots and leaves.

6. Ambiguities Eats, shoots and leaves. VBZ VBZ CC VBZ

7. Ambiguities Eats shoots and leaves. VBZ NNS CC NNS

8. Question Answering • Lecture 1 (two weeks ago):Introduction; History of QA; Architecture of a QA system; Evaluation. • Lecture 2 (last week):Question Classification; NLP techniques for question analysis; Tokenisation; Lemmatisation; POS-tagging; Parsing; WordNet. • Lecture 3 (today):Named Entity Recognition; Anaphora Resolution; Matching; Reranking;Answer Validation.

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

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

11. Recall the Answer-Type Taxonomy • We divided questions according to their expected answer type • Simple Answer-Type Typology • PERSON • NUMERAL • DATE • MEASURE • LOCATION • ORGANISATION • ENTITY

12. Named Entity Recognition • In order to make use of the answer types, we need to be able to recognisenamedentities of the same types in the corpus • PERSON • NUMERAL • DATE • MEASURE • LOCATION • ORGANISATION • ENTITY

13. Example Text Italy’s business world was rocked by the announcementlast Thursday that Mr.Verdi would leave his job as vice-president ofMusic Masters of Milan, Inc to become operations director ofArthur Andersen.

14. Named Entity Recognition <ENAMEX TYPE=„LOCATION“>Italy</ENAME>‘s business world was rocked by the announcement <TIMEX TYPE=„DATE“>last Thursday</TIMEX> that Mr. <ENAMEX TYPE=„PERSON“>Verdi</ENAMEX> would leave his job as vice-president of <ENAMEX TYPE=„ORGANIZATION“>Music Masters of Milan, Inc</ENAMEX> to become operations director of  <ENAMEX TYPE=„ORGANIZATION“>Arthur Andersen</ENAMEX>.

15. NER difficulties • Several types of entities are too numerous to include in dictionaries • New names turn up every day • Different forms of same entities in same text • Brian Jones … Mr. Jones • Capitalisation

16. NER approaches • Rule-based approach • Hand-crafted rules • Help from databases of known named entities • Statistical approach • Features • Machine learning

17. Anaphora

18. What is anaphora? • Relation between a pronoun and another element in the same or earlier sentence • Anaphoric pronouns: • he, she, it, they • Anaphoric noun phrases: • the country, • that idiot, • his hat, her dress

19. Anaphora (pronouns) • Question:What is the biggest sector in Andorra’s economy? • Corpus:Andorra is a tiny land-locked country in southwestern Europe, between France and Spain. Tourism, the largest sector of its tiny, well-to-do economy, accounts for roughly 80% of the GDP. • Answer: ?

20. Anaphora (definite descriptions) • Question:What is the biggest sector in Andorra’s economy? • Corpus:Andorra is a tiny land-locked country in southwestern Europe, between France and Spain. Tourism, the largest sector of the country’s tiny, well-to-do economy, accounts for roughly 80% of the GDP. • Answer: ?

21. Anaphora Resolution • Anaphora Resolution is the task of finding the antecedents of anaphoric expressions • Example system: • Mitkov, Evans & Orasan (2002) • http://clg.wlv.ac.uk/MARS/

22. Anaphora (pronouns) • Question:What is the biggest sector in Andorra’s economy? • Corpus:Andorra is a tiny land-locked country in southwestern Europe, between France and Spain. Tourism, the largest sector of Andorra’s tiny, well-to-do economy, accounts for roughly 80% of the GDP. • Answer: Tourism

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

24. Matching • Given a question and an expression with a potential answer, calculate a matching score S = match(Q,A) that indicates how well Q matches A • Example • Q: When was Franz Kafka born? • A1: Franz Kafka died in 1924. • A2: Kafka was born in 1883.

25. Semantic Matching Q: answer(X) franz(Y) kafka(Y) born(E) patient(E,Y) temp(E,X) A1: franz(x1) kafka(x1) die(x3) agent(x3,x1) in(x3,x2) 1924(x2)

26. Semantic Matching Q: answer(X) franz(Y) kafka(Y) born(E) patient(E,Y) temp(E,X) A1: franz(x1) kafka(x1) die(x3) agent(x3,x1) in(x3,x2) 1924(x2) X=x2

27. Semantic Matching Q: answer(x2) franz(Y) kafka(Y) born(E) patient(E,Y) temp(E,x2) A1: franz(x1) kafka(x1) die(x3) agent(x3,x1) in(x3,x2) 1924(x2) Y=x1

28. Semantic Matching Q: answer(x2) franz(x1) kafka(x1) born(E) patient(E,Y) temp(E,x2) A1: franz(x1) kafka(x1) die(x3) agent(x3,x1) in(x3,x2) 1924(x2) Y=x1

29. Semantic Matching Q: answer(x2) franz(x1) kafka(x1) born(E) patient(E,Y) temp(E,x2) A1: franz(x1) kafka(x1) die(x3) agent(x3,x1) in(x3,x2) 1924(x2) Match score = 3/6 = 0.50

30. Semantic Matching Q: answer(X) franz(Y) kafka(Y) born(E) patient(E,Y) temp(E,X) A2: kafka(x1) born(x3) patient(x3,x1) in(x3,x2) 1883(x2)

31. Semantic Matching Q: answer(X) franz(Y) kafka(Y) born(E) patient(E,Y) temp(E,X) A2: kafka(x1) born(x3) patient(x3,x1) in(x3,x2) 1883(x2) X=x2

32. Semantic Matching Q: answer(x2) franz(Y) kafka(Y) born(E) patient(E,Y) temp(E,x2) A2: kafka(x1) born(x3) patient(x3,x1) in(x3,x2) 1883(x2) Y=x1

33. Semantic Matching Q: answer(x2) franz(x1) kafka(x1) born(E) patient(E,x1) temp(E,x2) A2: kafka(x1) born(x3) patient(x3,x1) in(x3,x2) 1883(x2) E=x3

34. Semantic Matching Q: answer(x2) franz(x1) kafka(x1) born(x3) patient(x3,x1) temp(x3,x2) A2: kafka(x1) born(x3) patient(x3,x1) in(x3,x2) 1883(x2) E=x3

35. Semantic Matching Q: answer(x2) franz(x1) kafka(x1) born(x3) patient(x3,x1) temp(x3,x2) A2: kafka(x1) born(x3) patient(x3,x1) in(x3,x2) 1883(x2) Match score = 4/6 = 0.67

36. Matching Techniques • Weighted matching • Higher weight for named entities • WordNet • Hyponyms • Inferences rules • Example: BORN(E) & IN(E,Y) & DATE(Y)  TEMP(E,Y)

37. Reranking

38. Reranking • Most QA systems first produce a list of possible answers… • This is usually followed by a process called reranking • Reranking promotes correct answers to a higher rank

39. Factors in reranking • Matching score • The better the match with the question, the more likely the answers • Frequency • If the same answer occurs many times, it is likely to be correct

40. Sanity Checking Answer should be informative Q: Who is Tom Cruise married to? A: Tom Cruise Q: Where was Florence Nightingale born? A: Florence

41. Answer Validation • Given a ranked list of answers, some of these might not make sense at all • Promote answers that make sense • How? • Use even a larger corpus! • “Sloppy” approach • “Strict” approach

42. The World Wide Web

43. Answer validation (sloppy) • Given a question Q and a set of answers A1…An • For each i, generate query Q Ai • Count the number of hits for each i • Choose Ai with most number of hits • Use existing search engines • Google, AltaVista • Magnini et al. 2002 (CCP)

44. Corrected Conditional Probability • Treat Q and A as a bag of words • Q = content words question • A = answer hits(A NEAR Q) • CCP(Qsp,Asp) = ------------------------------ hits(A) x hits(Q) • Accept answers above a certain CCP threshold

45. Answer validation (strict) • Given a question Q and a set of answers A1…An • Create a declarative sentence with the focus of the question replaced by Ai • Use the strict search option in Google • High precision • Low recall • Any terms of the target not in the sentence as added to the query

46. Example • TREC 99.3Target: Woody Guthrie.Question: Where was Guthrie born? • Top-5 Answers: 1) Britain * 2) Okemah, Okla.3) Newport * 4) Oklahoma5) New York

47. Example: generate queries • TREC 99.3Target: Woody Guthrie.Question: Where was Guthrie born? • Generated queries: 1) “Guthrie was born in Britain” 2) “Guthrie was born in Okemah, Okla.”3) “Guthrie was born in Newport”4) “Guthrie was born in Oklahoma”5) “Guthrie was born in New York”

48. Example: add target words • TREC 99.3Target: Woody Guthrie.Question: Where was Guthrie born? • Generated queries: 1) “Guthrie was born in Britain” Woody 2) “Guthrie was born in Okemah, Okla.” Woody3) “Guthrie was born in Newport” Woody4) “Guthrie was born in Oklahoma” Woody5) “Guthrie was born in New York” Woody

49. Example: morphological variants TREC 99.3 Target: Woody Guthrie. Question: Where was Guthrie born? Generated queries: “Guthrie is OR was OR are OR were born in Britain” Woody “Guthrie is OR was OR are OR were born in Okemah, Okla.” Woody “Guthrie is OR was OR are OR were born in Newport” Woody “Guthrie is OR was OR are OR were born in Oklahoma” Woody “Guthrie is OR was OR are OR were born in New York” Woody

50. Example: google hits TREC 99.3 Target: Woody Guthrie. Question: Where was Guthrie born? Generated queries: “Guthrie is OR was OR are OR were born in Britain” Woody 0 “Guthrie is OR was OR are OR were born in Okemah, Okla.” Woody 10 “Guthrie is OR was OR are OR were born in Newport” Woody 0 “Guthrie is OR was OR are OR were born in Oklahoma” Woody 42 “Guthrie is OR was OR are OR were born in New York” Woody 2