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© Johan Bos April 2008 Question Answering (QA) Lecture 1 What is QA? Query Log Analysis Challenges in QA History of QA System Architecture Methods System.

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Presentation on theme: "© Johan Bos April 2008 Question Answering (QA) Lecture 1 What is QA? Query Log Analysis Challenges in QA History of QA System Architecture Methods System."— Presentation transcript:

1 © Johan Bos April 2008 Question Answering (QA) Lecture 1 What is QA? Query Log Analysis Challenges in QA History of QA System Architecture Methods System Evaluation State-of-the-art Lecture 2 Question Analysis Background Knowledge Answer Typing Lecture 3 Query Generation Document Analysis Semantic Indexing Answer Extraction Selection and Ranking

2 © Johan Bos April 2008 knowledge parsing boxing query answer typing Indri answer extraction answer selection answer reranking question answer ccg drs WordNet NomLex Indexed Documents Pronto architecture

3 © Johan Bos April 2008 knowledge parsing boxing query answer typing Indri answer extraction answer selection answer reranking question answer ccg drs WordNet NomLex Indexed Documents Lecture 3

4 © Johan Bos April 2008 Question Answering Lecture 3 Query Generation Document Analysis Semantic Indexing Answer Extraction Selection and Ranking

5 © Johan Bos April 2008 knowledge parsing boxing query answer typing Indri answer extraction answer selection answer reranking question answer ccg drs WordNet NomLex Indexed Documents Architecture of PRONTO

6 © Johan Bos April 2008 Query Generation Once we analysed the question, we need to retrieve appropriate documents Most QA systems use an off-the-shelf information retrieval system for this task Examples: –Lemur –Lucene –Indri (used by Pronto) The input of the IR system is a query; the output is a ranked set of documents

7 © Johan Bos April 2008 Queries Query generation depends on the way documents are indexed Based on –Semantic analysis of the question –Expected answer type –Background knowledge Computing a good query is hard – we dont want too little documents, and we dont want too many!

8 © Johan Bos April 2008 Generating Query Terms Example 1: –Question: Who discovered prions? –Text A: Dr. Stanley Prusiner received the Nobel prize for the discovery of prions. –Text B: Prions are a kind of proteins that… Query terms?

9 © Johan Bos April 2008 Generating Query Terms Example 2: –Question: When did Franz Kafka die? –Text A: Kafka died in –Text B: Dr. Franz died in Query terms?

10 © Johan Bos April 2008 Generating Query Terms Example 3: –Question: How did actor James Dean die? –Text: James Dean was killed in a car accident. Query terms?

11 © Johan Bos April 2008 Useful query terms Ranked on importance: –Named entities –Dates or time expressions –Expressions in quotes –Nouns –Verbs Queries can be expanded using the created local knowledge base

12 © Johan Bos April 2008 Query expansion example Query: sacajawea Returns only five documents Use synonyms in query expansions New query: sacajawea OR sagajawea Returns two hundred documents TREC 44.6 ( Sacajawea ) How much is the Sacajawea coin worth?

13 © Johan Bos April 2008 knowledge parsing boxing query answer typing Indri answer extraction answer selection answer reranking question answer ccg drs WordNet NomLex Indexed Documents Architecture of PRONTO

14 © Johan Bos April 2008 Question Answering Lecture 3 Query Generation Document Analysis Semantic Indexing Answer Extraction Selection and Ranking

15 © Johan Bos April 2008 Document Analysis – Why? The aim of QA is to output answers, not documents We need document analysis to –Find the correct type of answer in the documents –Calculate the probability that an answer is correct Semantic analysis is important to get valid answers

16 © Johan Bos April 2008 Document Analysis – When? After retrieval –token or word based index –keyword queries –low precision Before retrieval –semantic indexing –concept queries –high precision –More NLP required

17 © Johan Bos April 2008 Document Analysis – How? Ideally use the same NLP tools as for question analysis –This will make the semantic matching of Question and Answer easier –Not always possible: wide coverage tools are usally good at analysing text, but not at analysing questions –Questions are often not part of large annotated corpora, on which NLP tools are trained

18 © Johan Bos April 2008 Documents vs Passages Split documents into smaller passages –This will make the semantic matching faster and more accurate –In Pronto the passage size is two sentences, implemented by a sliding window Too small passages risk losing important contextual information –Pronouns and referring expressions

19 © Johan Bos April 2008 Document Analysis Tokenisation Part of speech tagging Lemmatisation Syntactic analysis (Parsing) Semantic analysis (Boxing) Named entity recognition Anaphora resolution

20 © Johan Bos April 2008 Why semantics is important Example: –Question: When did Franz Kafka die? –Text A: The mother of Franz Kafka died in 1918.

21 © Johan Bos April 2008 Why semantics is important Example: –Question: When did Franz Kafka die? –Text A: The mother of Franz Kafka died in –Text B: Kafka lived in Austria. He died in 1924.

22 © Johan Bos April 2008 Why semantics is important Example: –Question: When did Franz Kafka die? –Text A: The mother of Franz Kafka died in –Text B: Kafka lived in Austria. He died in –Text C: Both Kafka and Lenin died in 1924.

23 © Johan Bos April 2008 Why semantics is important Example: –Question: When did Franz Kafka die? –Text A: The mother of Franz Kafka died in –Text B: Kafka lived in Austria. He died in –Text C: Both Kafka and Lenin died in –Text D: Max Brod, who knew Kafka, died in 1930.

24 © Johan Bos April 2008 Why semantics is important Example: –Question: When did Franz Kafka die? –Text A: The mother of Franz Kafka died in –Text B: Kafka lived in Austria. He died in –Text C: Both Kafka and Lenin died in –Text D: Max Brod, who knew Kafka, died in –Text E: Someone who knew Kafka died in 1930.

25 © Johan Bos April 2008 DRS for The mother of Franz Kafka died in _____________________ | x3 x4 x2 x1 | | | | mother(x3) | | named(x4,kafka,per) | | named(x4,franz,per) | | die(x2) | | thing(x1) | | event(x2) | | of(x3,x4) | | agent(x2,x3) | | in(x2,x1) | | timex(x1)=+1918XXXX | |_____________________|

26 © Johan Bos April 2008 DRS for: Kafka lived in Austria. He died in _______________________ _____________________ | x3 x1 x2 | | x5 x4 | | | | | (| male(x3) |+| die(x5) |) | named(x3,kafka,per) | | thing(x4) | | live(x1) | | event(x5) | | agent(x1,x3) | | agent(x5,x3) | | named(x2,austria,loc) | | in(x5,x4) | | event(x1) | | timex(x4)=+1924XXXX | | in(x1,x2) | |_____________________| |_______________________|

27 © Johan Bos April 2008 DRS for: Both Kafka and Lenin died in _____________________ | x6 x5 x4 x3 x2 x1 | | | | named(x6,kafka,per) | | die(x5) | | event(x5) | | agent(x5,x6) | | in(x5,x4) | | timex(x4)=+1924XXXX | | named(x3,lenin,per) | | die(x2) | | event(x2) | | agent(x2,x3) | | in(x2,x1) | | timex(x1)=+1924XXXX | |_____________________|

28 © Johan Bos April 2008 DRS for: Max Brod, who knew Kafka, died in _____________________ | x3 x5 x4 x2 x1 | | | | named(x3,brod,per) | | named(x3,max,per) | | named(x5,kafka,per) | | know(x4) | | event(x4) | | agent(x4,x3) | | patient(x4,x5) | | die(x2) | | event(x2) | | agent(x2,x3) | | in(x2,x1) | | timex(x1)=+1930XXXX | |_____________________|

29 © Johan Bos April 2008 DRS for: Someone who knew Kafka died in _____________________ | x3 x5 x4 x2 x1 | | | | person(x3) | | named(x5,kafka,per) | | know(x4) | | event(x4) | | agent(x4,x3) | | patient(x4,x5) | | die(x2) | | event(x2) | | agent(x2,x3) | | in(x2,x1) | | timex(x1)=+1930XXXX | |_____________________|

30 © Johan Bos April 2008 Document Analysis Tokenisation Part of speech tagging Lemmatisation Syntactic analysis (Parsing) Semantic analysis (Boxing) Named entity recognition Anaphora resolution

31 © Johan Bos April 2008 Recall the Answer-Type Taxonomy We divided questions according to their expected answer type Simple Answer-Type Taxonomy PERSON NUMERAL DATE MEASURE LOCATION ORGANISATION ENTITY

32 © Johan Bos April 2008 Named Entity Recognition In order to make use of the answer types, we need to be able to recognise named entities of the same types in the documents PERSON NUMERAL DATE MEASURE LOCATION ORGANISATION ENTITY

33 © Johan Bos April 2008 Example Text Italys business world was rocked by the announcement last Thursday that Mr. Verdi would leave his job as vice-president of Music Masters of Milan, Inc to become operations director of Arthur Andersen.

34 © Johan Bos April 2008 Named entities Italys business world was rocked by the announcement last Thursday that Mr. Verdi would leave his job as vice-president of Music Masters of Milan, Inc to become operations director of Arthur Andersen.

35 © Johan Bos April 2008 Named Entity Recognition Italy s business world was rocked by the announcement last Thursday that Mr. Verdi would leave his job as vice-president of Music Masters of Milan, Inc to become operations director of Arthur Andersen.

36 © Johan Bos April 2008 NER difficulties Several types of entities are too numerous to include in dictionaries New names turn up every day Ambiguities –Paris, Lazio Different forms of same entities in same text –Brian Jones … Mr. Jones Capitalisation

37 © Johan Bos April 2008 NER approaches Rule-based approaches –Hand-crafted rules –Help from databases of known named entities [e.g. locations] Statistical approaches –Features –Machine learning

38 © Johan Bos April 2008 Document Analysis Tokenisation Part of speech tagging Lemmatisation Syntactic analysis (Parsing) Semantic analysis (Boxing) Named entity recognition Anaphora resolution

39 © Johan Bos April 2008 What is anaphora? Relation between a pronoun and another element in the same or earlier sentence Anaphoric pronouns: –he, him, she, her, it, they, them Anaphoric noun phrases: –the country, –these documents, –his hat, her dress

40 © Johan Bos April 2008 Anaphora (pronouns) Question: What is the biggest sector in Andorras 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: ?

41 © Johan Bos April 2008 Anaphora (definite descriptions) Question: What is the biggest sector in Andorras economy? Corpus: Andorra is a tiny land-locked country in southwestern Europe, between France and Spain. Tourism, the largest sector of the countrys tiny, well-to-do economy, accounts for roughly 80% of the GDP. Answer: ?

42 © Johan Bos April 2008 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/

43 © Johan Bos April 2008 Kafka lived in Austria. He died in _______________________ _____________________ | x3 x1 x2 | | x5 x4 | | | | | (| male(x3) |+| die(x5) |) | named(x3,kafka,per) | | thing(x4) | | live(x1) | | event(x5) | | agent(x1,x3) | | agent(x5,x3) | | named(x2,austria,loc) | | in(x5,x4) | | event(x1) | | timex(x4)=+1924XXXX | | in(x1,x2) | |_____________________| |_______________________|

44 © Johan Bos April 2008 Kafka lived in Austria. He died in _______________________ _____________________ | x3 x1 x2 | | x5 x4 | | | | | (| male(x3) |+| die(x5) |) | named(x3,kafka,per) | | thing(x4) | | live(x1) | | event(x5) | | agent(x1,x3) | | agent(x5,x3) | | named(x2,austria,loc) | | in(x5,x4) | | event(x1) | | timex(x4)=+1924XXXX | | in(x1,x2) | |_____________________| |_______________________|

45 © Johan Bos April 2008 Co-reference resolution Question: What is the biggest sector in Andorras economy? Corpus: Andorra is a tiny land-locked country in southwestern Europe, between France and Spain. Tourism, the largest sector of Andorras tiny, well- to-do economy, accounts for roughly 80% of the GDP. Answer: Tourism

46 © Johan Bos April 2008 Question Answering Lecture 3 Query Generation Document Analysis Semantic Indexing Answer Extraction Selection and Ranking

47 © Johan Bos April 2008 knowledge parsing boxing query answer typing Indri answer extraction answer selection answer reranking question answer ccg drs WordNet NomLex Indexed Documents Architecture of PRONTO

48 © Johan Bos April 2008 Semantic indexing If we index documents on the token level, we cannot search for specific semantic concepts If we index documents on semantic concepts, we can formulate more specific queries Semantic indexing requires a complete preprocessing of the entire document collection [can be costly]

49 © Johan Bos April 2008 Semantic indexing example Example NL question: When did Franz Kafka die? Term-based –query: kafka –Returns all passages containing the term kafka"

50 © Johan Bos April 2008 Semantic indexing example Example NL question: When did Franz Kafka die? Term-based –query: kafka –Returns all passages containing the term kafka" Concept-based –query: DATE & kafka –Returns all passages containing the term "kafka" and a date expression

51 © Johan Bos April 2008 Question Answering Lecture 3 Query Generation Document Analysis Semantic Indexing Answer Extraction Selection and Ranking

52 © Johan Bos April 2008 knowledge parsing boxing query answer typing Indri answer extraction answer selection answer reranking question answer ccg drs WordNet NomLex Indexed Documents Architecture of PRONTO

53 © Johan Bos April 2008 Answer extraction Passage retrieval gives us a set of ranked documents Match answer with question –DRS for question –DRS for each possible document –Score for amount of overlap Deep inference or shallow matching Use knowledge

54 © Johan Bos April 2008 Answer extraction: 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? –A 1 : Franz Kafka died in –A 2 : Kafka was born in 1883.

55 © Johan Bos April 2008 Using logical inference Recall that Boxer produces first order representations [DRSs] In theory we could use a theorem prover to check whether a retrieved passage entails or is inconsistent with a question In practice this is too costly, given the high number of possible answer + question pairs that need to be considered Also: theorem provers are precise – they dont give us information if they almost find a proof, although this would be useful for QA

56 © Johan Bos April 2008 Semantic matching Matching is an efficient approximation to the inference task Consider flat semantic representation of the passage and the question Matching gives a score of the amount of overlap between the semantic content of the question and a potential answer

57 © Johan Bos April 2008 Matching Example Question: When was Franz Kafka born? Passage 1: Franz Kafka died in Passage 2: Kafka was born in 1883.

58 © Johan Bos April 2008 Semantic Matching [1] answer(X) franz(Y) kafka(Y) born(E) patient(E,Y) temp(E,X) franz(x1) kafka(x1) die(x3) agent(x3,x1) in(x3,x2) 1924(x2) Q:A1:A1:

59 © Johan Bos April 2008 Semantic Matching [1] answer(X) franz(Y) kafka(Y) born(E) patient(E,Y) temp(E,X) franz(x1) kafka(x1) die(x3) agent(x3,x1) in(x3,x2) 1924(x2) Q:A1:A1: X=x2

60 © Johan Bos April 2008 Semantic Matching [1] answer(x2) franz(Y) kafka(Y) born(E) patient(E,Y) temp(E,x2) franz(x1) kafka(x1) die(x3) agent(x3,x1) in(x3,x2) 1924(x2) Q:A1:A1:

61 © Johan Bos April 2008 Semantic Matching [1] answer(x2) franz(Y) kafka(Y) born(E) patient(E,Y) temp(E,x2) franz(x1) kafka(x1) die(x3) agent(x3,x1) in(x3,x2) 1924(x2) Q:A1:A1: Y=x1

62 © Johan Bos April 2008 Semantic Matching [1] answer(x2) franz(x1) kafka(x1) born(E) patient(E,x1) temp(E,x2) franz(x1) kafka(x1) die(x3) agent(x3,x1) in(x3,x2) 1924(x2) Q:A1:A1:

63 © Johan Bos April 2008 Semantic Matching [1] answer(x2) franz(x1) kafka(x1) born(E) patient(E,x1) temp(E,x2) franz(x1) kafka(x1) die(x3) agent(x3,x1) in(x3,x2) 1924(x2) Q:A1:A1:

64 © Johan Bos April 2008 Semantic Matching [1] answer(x2) franz(x1) kafka(x1) born(E) patient(E,x1) temp(E,x2) Match score = 3/6 = 0.50 Q:A1:A1: franz(x1) kafka(x1) die(x3) agent(x3,x1) in(x3,x2) 1924(x2)

65 © Johan Bos April 2008 Semantic Matching [2] answer(X) franz(Y) kafka(Y) born(E) patient(E,Y) temp(E,X) kafka(x1) born(x3) patient(x3,x1) in(x3,x2) 1883(x2) Q:A2:A2:

66 © Johan Bos April 2008 Semantic Matching [2] answer(X) franz(Y) kafka(Y) born(E) patient(E,Y) temp(E,X) kafka(x1) born(x3) patient(x3,x1) in(x3,x2) 1883(x2) Q:A2:A2: X=x2

67 © Johan Bos April 2008 Semantic Matching [2] answer(x2) franz(Y) kafka(Y) born(E) patient(E,Y) temp(E,x2) kafka(x1) born(x3) patient(x3,x1) in(x3,x2) 1883(x2) Q:A2:A2:

68 © Johan Bos April 2008 Semantic Matching [2] answer(x2) franz(Y) kafka(Y) born(E) patient(E,Y) temp(E,x2) kafka(x1) born(x3) patient(x3,x1) in(x3,x2) 1883(x2) Q:A2:A2: Y=x1

69 © Johan Bos April 2008 Semantic Matching [2] answer(x2) franz(x1) kafka(x1) born(E) patient(E,x1) temp(E,x2) kafka(x1) born(x3) patient(x3,x1) in(x3,x2) 1883(x2) Q:A2:A2:

70 © Johan Bos April 2008 Semantic Matching [2] answer(x2) franz(x1) kafka(x1) born(E) patient(E,x1) temp(E,x2) kafka(x1) born(x3) patient(x3,x1) in(x3,x2) 1883(x2) Q:A2:A2: E=x3

71 © Johan Bos April 2008 Semantic Matching [2] answer(x2) franz(x1) kafka(x1) born(x3) patient(x3,x1) temp(x3,x2) kafka(x1) born(x3) patient(x3,x1) in(x3,x2) 1883(x2) Q:A2:A2:

72 © Johan Bos April 2008 Semantic Matching [2] answer(x2) franz(x1) kafka(x1) born(x3) patient(x3,x1) temp(x3,x2) kafka(x1) born(x3) patient(x3,x1) in(x3,x2) 1883(x2) Q:A2:A2:

73 © Johan Bos April 2008 Semantic Matching [2] answer(x2) franz(x1) kafka(x1) born(x3) patient(x3,x1) temp(x3,x2) kafka(x1) born(x3) patient(x3,x1) in(x3,x2) 1883(x2) Q:A2:A2: Match score = 4/6 = 0.67

74 © Johan Bos April 2008 Matching Example Question: When was Franz Kafka born? Passage 1: Franz Kafka died in Passage 2: Kafka was born in Match score = 0.67 Match score = 0.50

75 © Johan Bos April 2008 Matching Techniques Weighted matching –Higher weight for named entities –Estimate weights using machine learning Incorporate background knowledge –WordNet [hyponyms] –NomLex –Paraphrases: BORN(E) & IN(E,Y) & DATE(Y) TEMP(E,Y)

76 © Johan Bos April 2008 Question Answering Lecture 3 Query Generation Document Analysis Semantic Indexing Answer Extraction Selection and Ranking

77 © Johan Bos April 2008 knowledge parsing boxing query answer typing Indri answer extraction answer selection answer reranking question answer ccg drs WordNet NomLex Indexed Documents Architecture of PRONTO

78 © Johan Bos April 2008 Answer selection Rank answer –Group duplicates –Syntactically or semantically equivalent –Sort on frequency How specific should an answer be? –Semantic relations between answers –Hyponyms, synonyms –Answer modelling [PhD thesis Dalmas 2007] Answer cardinality

79 © Johan Bos April 2008 Answer selection example 1 Where did Franz Kafka die? –In his bed –In a sanatorium –In Kierling –Near Vienna –In Austria –In Berlin –In Germany

80 © Johan Bos April 2008 Answer selection example 2 Where is 3M based? –In Maplewood –In Maplewood, Minn. –In Minnesota –In the U.S. –In Maplewood, Minn., USA –In San Francisco –In the Netherlands

81 © Johan Bos April 2008 knowledge parsing boxing query answer typing Indri answer extraction answer selection answer reranking question answer ccg drs WordNet NomLex Indexed Documents Architecture of PRONTO

82 © Johan Bos April 2008 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

83 © Johan Bos April 2008 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

84 © Johan Bos April 2008 Answer Validation –check whether an answer is likely to be correct using an expensive method Tie breaking –Deciding between two answers with similar probability Methods: –Inference check –Sanity checking –Googling

85 © Johan Bos April 2008 Inference check Use first-order logic [FOL] to check whether a potential answer entails the question This can be done with the use of a theorem prover –Translate Q into FOL –Translate A into FOL –Translate background knowledge into FOL –If ((BK fol & A fol ) Q fol ) is a theorem, we have a likely answer

86 © Johan Bos April 2008 Sanity Checking Answer should be informative, that is, not part of the question Q: Who is Tom Cruise married to? A: Tom Cruise Q: Where was Florence Nightingale born? A: Florence

87 © Johan Bos April 2008 Googling 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

88 © Johan Bos April 2008 The World Wide Web

89 © Johan Bos April 2008 Answer validation (sloppy) Given a question Q and a set of answers A 1 …A n For each i, generate query Q A i Count the number of hits for each i Choose A i with most number of hits Use existing search engines –Google, AltaVista –Magnini et al (CCP)

90 © Johan Bos April 2008 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

91 © Johan Bos April 2008 Answer validation (strict) Given a question Q and a set of answers A 1 …A n Create a declarative sentence with the focus of the question replaced by A i 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

92 © Johan Bos April 2008 Example TREC 99.3 Target: Woody Guthrie. Question: Where was Guthrie born? Top-5 Answers: 1) Britain * 2) Okemah, Okla. 3) Newport * 4) Oklahoma 5) New York

93 © Johan Bos April 2008 Example: generate queries TREC 99.3 Target: 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

94 © Johan Bos April 2008 Example: add target words TREC 99.3 Target: Woody Guthrie. Question: Where was Guthrie born? Generated queries: 1) Guthrie was born in Britain Woody 2) Guthrie was born in Okemah, Okla. Woody 3) Guthrie was born in Newport Woody 4) Guthrie was born in Oklahoma Woody 5) Guthrie was born in New York Woody

95 © Johan Bos April 2008 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

96 © Johan Bos April 2008 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

97 © Johan Bos April 2008 Example: reranked answers TREC 99.3 Target: Woody Guthrie. Question: Where was Guthrie born? Original answers 1) Britain * 2) Okemah, Okla. 3) Newport * 4) Oklahoma 5) New York Reranked answers * 4) Oklahoma * 2) Okemah, Okla. 5) New York 1) Britain 3) Newport

98 © Johan Bos April 2008 Question Answering (QA) Lecture 1 What is QA? Query Log Analysis Challenges in QA History of QA System Architecture Methods System Evaluation State-of-the-art Lecture 2 Question Analysis Background Knowledge Answer Typing Lecture 3 Query Generation Document Analysis Semantic Indexing Answer Extraction Selection and Ranking

99 © Johan Bos April 2008 Where to go from here Producing answers in real-time Improve accuracy Answer explanation User modelling Speech interfaces Dialogue (interactive QA) Multi-lingual QA Non sequential architectures


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