Браславский: вопросно-ответный поиск

Вопросно‐ответный поискВопросно‐ответный поиск в интернетер

Павел БраславскийбNLP seminar, СПб

18.12.2010

Использованы слайды: Chris Manning, Eugene Agichtein

ПланПлан

• Почему «ответы на вопросы»?

• «Традиционный» вопросно‐ответный поиск«Традиционный» вопросно ответный поиск

• Ответы на вопросы в Вебе

• Социальный вопросно‐ответный поиск

• ЗаключениеЗаключение

218.12.2010 Павел Браславский

ИдеяИдея

ВопросИсчерпывающая и естественная формулировка р щ ф р у ринформационной потребности

ОтветОтветТочный ответ или фрагмент документа, содержащий ответ

ДанныеТекстовая коллекция


Запросы вопросы: 2 3%Запросы – вопросы: 2‐3%

http://company.yandex.ru/facts/researches/ya_search_2009.xml

Павел Браславский ‐ Анализ запросов 4

Близкие областиБлизкие области

• ЕЯ‐интерфейс к БД

• Диалоговые системыДиалоговые системы


Примеры вопросов TRECПримеры вопросов TREC

1 Wh i th th f th b k "Th I L d A Bi h f1. Who is the author of the book, "The Iron Lady: A Biography of Margaret Thatcher"?

2. What was the monetary value of the Nobel Peace Prize in 1989?3. What does the Peugeot company manufacture?4. How much did Mercury spend on advertising in 1993?5. What is the name of the managing director of Apricot Computer?5. What is the name of the managing director of Apricot Computer?6. Why did David Koresh ask the FBI for a word processor?7. What debts did Qintex group leave?8 Wh i h f h l i l di i h8. What is the name of the rare neurological disease with symptoms

such as: involuntary movements (tics), swearing, and incoherent vocalizations (grunts, shouts, etc.)?


[Manning]718.12.2010 Павел Браславский

18.12.2010 Павел Браславский 8[Manning]


Ключевые компонентыКлючевые компоненты

• ИПС (индексирование документов, извлечение и ранжирование фрагментов)р р фр )

• NER

ф (• Классификатор вопросов ( гипотезы ответа)

• Синтаксический и семантический анализ

М• Машина вывода

textual inference/entailment/reasoningg

18.12.2010 Павел Браславский 10

ПримерПример

• Сегодня в возрасте восьмидесяти лет в Переделкино умер глава русской р д у р руправославной церкви Алексий Второй.

• Патриарх Алексий II скончался 5 декабря• Патриарх Алексий II скончался 5 декабря 2008 года.


CLEF 2009CLEF 2009

bliQ 00 l l i• ResPubliQA: 500 natural language questions, systems must return the passage, multilingual ll ti f l i l ti d tcollection of legislation documents.

• QAST: written and oral questions (factual and definitional) in different languages are formulated against a set of audio recordings.

• GikiCLEF: open list questions over Wikipedia that require geographic reasoning, complex information extraction, and cross‐lingual processing.


GikiCLEFGikiCLEF• EX01 Name Portuguese speaking Nobel prize winners• EX01: Name Portuguese‐speaking Nobel prize winners• EX02: List Portuguese Pop/Rock groups created in the 90s. • EX03: Which Brazilian football players play in clubs in the Iberian

Pensinsula?Pensinsula? • EX04: What capitals of Dutch provinces received their town privileges

during the sixteenth century? • EX05: In which places did Italo Calvino live during adulthood?• EX05: In which places did Italo Calvino live during adulthood? • EX06: Name Mexican poets who published volumes with ballads until

1930. • EX07: Name authors born in Alaska and who wrote fiction about it• EX07: Name authors born in Alaska and who wrote fiction about it. • EX08: What Belgians won the Tour de France exactly twice? • EX09: Find Amazon tribes which have no written language

EX10 Fi d N h E i hi h d l i• EX10: Find Northern Europe companies which produce nano‐electroniccomponents for planes.

Павел Браславский 13

РОМИПРОМИП

К BYWEB• Коллекция BY.WEB• 10K запросов‐вопросов из лога поисковой машины

– gta san andreas как сделать машину призрак?g д у р р– монгольские полевки как ухаживать?– берут ли с экземой в армию?– перелёт до екатеринбурга от москвы сколько по времени?– черезсколько дней появляются корни у отростка традесканции?– всем ли девушкам важны деньги?– как заполучить парня своей мечты?

где пройдет финал кубка уефа 2009?– где пройдет финал кубка уефа 2009?

• До 5 ответов системы: docID, краткий ответ, фрагмент (до 300 символов)

http://romip.ru/ru/2010/tracks/qa.html


AskMSRAskMSRКлассификация вопроса (7 категорий)

взвешивание

Регулярные выражения



AnswerBusAnswerBus


Примеры системПримеры систем

• AnswerBus, PowerSet иLLC (http://www.languagecomputer.com/) ( p g g p )не работают

• EasyAsk AnswerLogic AnswerFriend Start• EasyAsk, AnswerLogic, AnswerFriend, Start, Quasm, Mulder, Webclopedia, ISI TextMap,

[ ]etc. [Manning]


Wolfram AlphaWolfram Alpha



Ask.com


Разные данные/подходыРазные данные/подходы

• Поиск ответа по коллекции текстов

• Поиск ответа в структурированных данныхПоиск ответа в структурированных данных

• Поиск ответа в коллекции вопросов и ответов– FAQ

– Онлайн консультации

– Форумы сообщества– Форумы, сообщества

– Специализированные социальные сервисы


Социальный поискСоциальный поиск

1. Поиск с помощью сообщества

2. Поиск с учетом социальных2. Поиск с учетом социальных взаимодействий пользователей

3 П й3. Поиск по контенту, который является результатом социальных взаимодействий



otvety.mail.ru


quora.com


Проблемы/задачиПроблемы/задачи

• Качество контента– Информация/общениеф р ц / щ

• Устранение избыточности (смысловые дубликаты)дубликаты)

• Релевантность/ранжирование

• Классификация запросов

З В• Запрос Вопрос


Finding High Quality Content in SMFinding High Quality Content in SM

E A i h i C C ill D D A Gi i d GE. Agichtein, C. Castillo, D. Donato, A. Gionis, and G. Mishne, Finding High Quality Content in Social Media inWSDM 2008

• Well‐written

Media, in WSDM 2008

• Interesting• Relevant (answer)

As judged by professional editorse e a (a s e )

• Factually correct• Popular?

professional editors

Popular?• Provocative?• Useful?


• Useful?

[Agichtein]

292929

How do Question and Answer Quality relate?

30[Agichtein]

313131

323232

333333

343434

CommunityCommunityCommunityCommunity

3535[Agichtein]

Link Analysis for Authority EstimationLink Analysis for Authority Estimation

Q ti 1Answer 1 User 3 User 3

Question 1

Answer 2

User 1User 4

User 1User 4

Question 2

Answer 4

Answer 3

User 2User 6

User 5

User 2 User 6

User 5

Answer 5

Answer 4 User 6

Question 3

Answer 6∑=

=Mi

iHjA0

)()(

∑=

=Kj

jAiH0

)()(= Mi ..0

H b ( k ) A th it ( )

36

= Kj ..0 Hub (asker) Authority (answerer)

[Agichtein]

Random forest classifier

373737

Result 1: Identifying High Quality Questions

38[Agichtein]

Top Features for Question ClassificationTop Features for Question Classification

A k l it (“ t ”)• Asker popularity (“stars”)

• Punctuation density

• Topical category

• Page views

• KL Divergence from reference corpus LM

39[Agichtein]

Identifying High Quality AnswersIdentifying High Quality Answers

40[Agichtein]

Top Features for Answer ClassificationTop Features for Answer Classification

A l th• Answer length

• Community ratings

Answerer reputation

• Word overlap

• Kincaid readability score

41[Agichtein]

Dimensions of “Quality”Dimensions of Quality

ll i• Well‐written• Interestingg• Relevant (answer)• Factually correct• Factually correct• Popular?• Timely?• Provocative?

As judged by the asker (or community)Provocative?

• Useful?

42[Agichtein]

Yahoo! Answers: The Good NewsYahoo! Answers: The Good News

• Active community of millions of users in many countries and languagesg g

Eff i f bj i i f i d• Effective for subjective information needs– Great forum for socialization/chat

C b i l bl f h d t fi d i f ti• Can be invaluable for hard‐to‐find information not available on the web

4343[Agichtein]

4444[Agichtein]

Yahoo! Answers: The Bad NewsMay have to wait a long time to get a satisfactory answer

Yahoo! Answers: The Bad News

1 FIFA World Cup

30

35

40 1. FIFA World Cup2. Optical3. Poetry

20

25

30 3. Poetry4. Football (American)5. Soccer

10

15 6. Medicine7. Winter Sports8 Special Education

0

5

1 2 3 4 5 6 7 8 9 10

8. Special Education9. General Health Care10. Outdoor Recreation

May never obtain a satisfying answer

10. Outdoor RecreationTime to close a question (hours)

May never obtain a satisfying answer4545

[Agichtein]

Y Liu J Bian and E Agichtein in SIGIR 2008

Predicting Asker SatisfactionY. Liu, J. Bian, and E. Agichtein, in SIGIR 2008

Given a question submitted by an asker in CQA, predict whether the user will be satisfied with thepredict whether the user will be satisfied with the answers contributed by the community.

– “Satisfied” :• The asker has closed the question AND• The asker has closed the question AND• Selected the best answer AND• Rated best answer >= 3 “stars” (# not important)• Rated best answer >= 3 stars (# not important)

– Else, “Unsatisfied

46[Agichtein]

ASP: Asker Satisfaction PredictionASP: Asker Satisfaction Prediction

Answerer History

TextCategory

Answerer History

Asker History

Answer

Question

Wikipedia

NewsClassifier

asker is asker is not

News

asker is satisfied

asker is not satisfied

47[Agichtein]

Experimental Setup: DataExperimental Setup: DataCrawled from Yahoo! Answers in early 2008

Questions Answers Askers Categories % Satisfied216,170 1,963,615 158,515 100 50.7%

“Anonymized” dataset available at: http://irmathcs emory edu/shared/http://ir.mathcs.emory.edu/shared/

/ h ! b “ h ”1/2009: Yahoo! Webscope : “Comprehensive” Answers dataset: ~5M questions & answers.

48[Agichtein]

Satisfaction by TopicSatisfaction by TopicTopic Questions Answers A per Q Satisfied Asker Time to close

b krating by asker2006 FIFA W ld C

1194 35,659 329.86 55.4% 2.63 47 iWorld Cup minutes

Mental H lth

151 1159 7.68 70.9% 4.30 1.5 daysHealth

Mathematics 651 2329 3.58 44.5% 4.48 33 minutesminutes

Diet & Fitness

450 2436 5.41 68.4% 4.30 1.5 daysFitness

49[Agichtein]

Satisfaction Prediction: Human JudgesSatisfaction Prediction: Human Judges

T th k ’ ti• Truth: asker’s rating• A random sample of 130 questions• Researchers

– Agreement: 0.82 F1: 0.45 2P*R/(P+R)

• Amazon Mechanical TurkAmazon Mechanical Turk– Five workers per question. – Agreement: 0.9 F1: 0.61Agreement: 0.9 F1: 0.61 – Best when at least 4 out of 5 raters agree

50[Agichtein]

Performance: ASP vs Humans (F1 Satisfied)Performance: ASP vs. Humans (F1, Satisfied)Classifier With Text Without Text Selected Features

ASP_SVM 0.69 0.72 0.62

ASP_C4.5 0.75 0.76 0.77ASP_RandomForest 0.70 0.74 0.68

ASP_Boosting 0.67 0.67 0.67

ASP_NB 0.61 0.65 0.58

Best Human Perf 0.61Baseline (random) 0.66

Human F1 is lower than the random baseline!

ASP is significantly more effective than humans

Human F1 is lower than the random baseline!

51

g y

Top Features by Information GainTop Features by Information Gain

0 14 Q A k ’ i ti• 0.14 Q: Askers’ previous rating• 0.14 Q: Average past rating by asker• 0.10 UH: Member since (interval)• 0.05 UH: Average # answers for by past Qg y p Q• 0.05 UH: Previous Q resolved for the asker• 0 04 CA: Average asker rating for category0.04 CA: Average asker rating for category• 0.04 UH: Total number of answers receivedreceived

…

52[Agichtein]

СсылкиСсылки

• В + М QA ( NLP• Видео + транскрипт лекции Маннига про QA (курс NLP, лекция 18) http://see.stanford.edu/see/courses.aspx

• Слайды лекции Маннига про QA htt // t f d d / l / 224 / ll b ht l#l 18http://www.stanford.edu/class/cs224n/syllabus.html#lec18

• РОМИП QA http://romip.ru/ru/2010/tracks/qa.html• QA @ TREC http://trec.nist.gov/data/qamain.htmlp g q• CLEF http://www.clef‐campaign.org/• AnswerBus http://answerbus.coli.uni‐sb.de/• Ответы@mail ru http://otvety mail ru/• Ответы@mail.ru http://otvety.mail.ru/• Yahoo! Answers http://answers.yahoo.com/• Quora http://www.quora.com/• Aardvark http://vark.com/• WolframAlpha http://www.wolframalpha.com/


СтатьиСтатьи• Dmitri Roussinov Weiguo Fan and Jose Robles Flores 2008 Beyond• Dmitri Roussinov, Weiguo Fan, and Jose Robles‐Flores. 2008. Beyond

keywords: Automated question answering on the web. Commun. ACM 51, 9.

• Kwok C Etzioni O and Weld D S Scaling Question Answering to the WebKwok C., Etzioni O. and Weld D.S. Scaling Question Answering to the Web. ACM TOIS, Vol. 19, No. 3, July 2001.

• Banko M. et al. AskMSR: Question Answering Using the Worldwide Web. In Proc. of 2002 AAAI Spring Symposium on Mining Answers from Texts p g y p gand Knowledge Bases.

• Zhiping Zheng. 2003. Question answering using web news as knowledge base. In Proceedings of the tenth conference on European chapter of the

i i f i l i i i l ( ' ) lAssociation for Computational Linguistics ‐ Volume 2 (EACL '03), Vol. 2. Association for Computational Linguistics, Morristown, NJ, USA, 251‐254.

• E. Agichtein, C. Castillo, D. Donato, A. Gionis, and G. Mishne, Finding High Quality Content in Social Media inWSDM 2008Quality Content in Social Media, in WSDM 2008

• Y. Liu, J. Bian, and E. Agichtein, Predicting Asker Satisfaction, SIGIR 2008


Спасибо за внимание!

Павел БраславскийПавел Браславский

pb@yandex‐team.ru


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Браславский: вопросно-ответный поиск