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7/18/2019 Multimedia Answer Generation for Community Question Answering
http://slidepdf.com/reader/full/multimedia-answer-generation-for-community-question-answering-56d4ed306f291 1/17
Multimedia Answer Generation for
Community Question Answering
7/18/2019 Multimedia Answer Generation for Community Question Answering
http://slidepdf.com/reader/full/multimedia-answer-generation-for-community-question-answering-56d4ed306f291 2/17
Problem Statement
• Textual Answers
• Multimedia Answers
7/18/2019 Multimedia Answer Generation for Community Question Answering
http://slidepdf.com/reader/full/multimedia-answer-generation-for-community-question-answering-56d4ed306f291 3/17
Literature Survey
Sr. No Author Year Contribution
1. Tre! Text "etrievalConferene#$tt%!&&tre.nist.gov '
1(() Text based QA
*. S.A. Quarteroni+ S.Manand$ar
*)), Text based QA based onty%e of -uestions%en /omain QA
0. /. Molla 2.L 3iedo *))4 "estrited /omain QA
5. 6. Cui+ M.7. 8an *))4 /e9nitional QA
:. ".C.;ang+ ;.;. Co$en+<. =yberg
*)), List QA
>. 6. 7ang+ T S $ua+ S.;ang
*))0 3ideo QA
4. 2.Cao+ 7?C? ;u+ 7?S Lee *))5?
*))(
3ideo QA using C"
AS"
?
7/18/2019 Multimedia Answer Generation for Community Question Answering
http://slidepdf.com/reader/full/multimedia-answer-generation-for-community-question-answering-56d4ed306f291 4/17
System /eom%osition
• Answer medium seletion+
• Query generation
• Multimedia data seletion and%resentation.
7/18/2019 Multimedia Answer Generation for Community Question Answering
http://slidepdf.com/reader/full/multimedia-answer-generation-for-community-question-answering-56d4ed306f291 5/17
Pre?re-uisites
• /atasets
• mage 3ideo Mining AP – Blir+ Piasaweb+ 7outube+ et.
7/18/2019 Multimedia Answer Generation for Community Question Answering
http://slidepdf.com/reader/full/multimedia-answer-generation-for-community-question-answering-56d4ed306f291 6/17
Proess Blow
/atasetolletion
Classi9ationDConversational
nformationalE
T$e answer mediumseletion and -uery
seletion om%onents
Query Generation forMultimedia "etrieval
Multimedia /ata
/u%liate "emoval
"esult "e?raning
7/18/2019 Multimedia Answer Generation for Community Question Answering
http://slidepdf.com/reader/full/multimedia-answer-generation-for-community-question-answering-56d4ed306f291 7/17
Answer Medium Seletion
• Classi9ation – only text+
– Text F image
– text F video
– text F image F video
• A%%roa$! – Question @ased Classi9ation – Answer @ased Classi9ation
– Media "esoure Analysis
7/18/2019 Multimedia Answer Generation for Community Question Answering
http://slidepdf.com/reader/full/multimedia-answer-generation-for-community-question-answering-56d4ed306f291 8/17
Query <xtration
For each QA pair, we generate threequeries.
1. Convert t$e -uestion to a -uery+
*. dentify several ey one%ts from verbose
answer w$i$ will $ave t$e maor im%at oneHetiveness.
0. Binally+ we ombine t$e two -ueries t$at are
generated from t$e -uestion and t$e answerres%etively.
7/18/2019 Multimedia Answer Generation for Community Question Answering
http://slidepdf.com/reader/full/multimedia-answer-generation-for-community-question-answering-56d4ed306f291 9/17
Query Generation for Multimedia
Sear$
• Query <xtration
• T$e seond ste% is -uery seletion.
7/18/2019 Multimedia Answer Generation for Community Question Answering
http://slidepdf.com/reader/full/multimedia-answer-generation-for-community-question-answering-56d4ed306f291 10/17
Query Seletion
• T$ree?lass lassi9ation tas+ sine we need to$oose one from t$e t$ree -ueries
• ;e ado%t t$e following features!
–
PS 6istogram.• Bor t$e -ueries t$at ontain a lot of om%lex verbs it will
be diIult to retrieve meaningful multimedia results.
• ;e use PS tagger to assign %art?of?s%ee$ to ea$ wordof bot$ -uestion and answer.
• 6ere we em%loy t$e Stanford Log?linear Part?f?S%ee$ Tagger and 0> PS are identi9ed.
• ;e t$en generate a 0>?dimensional $istogram+ in w$i$ea$ bin ounts t$e number of words belonging to t$eorres%onding ategory of %art?of?s%ee$.
7/18/2019 Multimedia Answer Generation for Community Question Answering
http://slidepdf.com/reader/full/multimedia-answer-generation-for-community-question-answering-56d4ed306f291 11/17
D*E Sear$ %erformane %redition.
– Clarity sore for ea$ -uery based on t$e 8L divergene between t$e -uery and olletion language models.
– ;e an generate >?dimensional sear$ %erformane%redition features in all Dt$ere are t$ree -ueries andsear$ is %erformed on bot$ image and video sear$enginesE.
• T$erefore+ for ea$ QA %air+ we an generate 5*?dimensional features.
• @ased on t$e extrated features+ we train an S3M
lassi9er wit$ a labeled training set for lassi9ation
• i.e.+ seleting one from t$e t$ree -ueries.
7/18/2019 Multimedia Answer Generation for Community Question Answering
http://slidepdf.com/reader/full/multimedia-answer-generation-for-community-question-answering-56d4ed306f291 12/17
Clarity funtion!
7/18/2019 Multimedia Answer Generation for Community Question Answering
http://slidepdf.com/reader/full/multimedia-answer-generation-for-community-question-answering-56d4ed306f291 13/17
Multimedia /ata Seletion
Predition
• ;e %erform sear$ using t$e generated -ueriesto ollet image and video data wit$ Googleimage and video sear$ engines res%etively.
• Most of t$e urrent ommerial sear$ enginesare built u%on text?based indexing and usuallyreturn a lot of irrelevant results.
• T$erefore+ – "e?raning by ex%loring visual information is essential
to reorder t$e initial text?based sear$ results.
– 6ere we ado%t t$e gra%$?based re?raning met$od.
7/18/2019 Multimedia Answer Generation for Community Question Answering
http://slidepdf.com/reader/full/multimedia-answer-generation-for-community-question-answering-56d4ed306f291 14/17
Gra%$ @ased "e?"aning
/u%liate "emoval
7/18/2019 Multimedia Answer Generation for Community Question Answering
http://slidepdf.com/reader/full/multimedia-answer-generation-for-community-question-answering-56d4ed306f291 15/17
List of Algorit$ms
• Core sentene extration from -uestion
• Stemming sto%?words removal on answers
• Question Ty%e based on Answer Medium D =aJve @ayesE
• 6ead word extration
• Media "esoure Analysis – Clarity sore based on 8L /ivergene
• Query Generation
• Query Seletion – PS Beature <xtration
– Sear$ Performane Predition
•Multimedia /ata seletion %resentation – Gra%$ based raning
– Bae /etetion Algorit$ms
– Beature <xtration from images
– 8ey frame identi9ation extration
7/18/2019 Multimedia Answer Generation for Community Question Answering
http://slidepdf.com/reader/full/multimedia-answer-generation-for-community-question-answering-56d4ed306f291 16/17
"eferenes
1. M. Surdeanu+ M. Ciaramita+ and 6. Karagoa+Learning to ran answers on large online QAolletions+N in Proc. Association forComputational Linguistics+ *)),
*. S. Cronen?Townsend+ 7. K$ou+ and;. @. Croft+Prediting -uery %erformane+N in Proc. ACM Int.SIGIR Conf.+ *))*.
0. Li-iang =ie+ Meng ;ang+ 7ue Gao+ K$eng?2un K$a+
and Tat?Seng C$ua @eyond Text QA! MultimediaAnswer Generation by 6arvesting ;ebnformationN <<< Multimedia Transation *)10
7/18/2019 Multimedia Answer Generation for Community Question Answering
http://slidepdf.com/reader/full/multimedia-answer-generation-for-community-question-answering-56d4ed306f291 17/17
T$an 7ouO.