DiQuest.com Intelligent Dialog Interface Solution for friendly User Interactions in Internet WEB...

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DiQuest.com

Intelligent Dialog Interface Solution Intelligent Dialog Interface Solution for friendly User Interactions in Internet WEB Environmentfor friendly User Interactions in Internet WEB Environment

March, 2001

Next generation web Next generation web search and Question-search and Question-answering technologyanswering technology

DiQuest.com

Intelligent Dialog Interface SolutionIntelligent Dialog Interface Solution for friendly User Interactions in Internet WEB Environmentfor friendly User Interactions in Internet WEB Environment

Oct, 2001

Gary Geunbae LeeGary Geunbae Lee

Dept. of CSE, Postech Dept. of CSE, Postech

& DiQuest.com& DiQuest.com

DiQuest.com Intelligent Dialog Interface Solution Intelligent Dialog Interface Solution for friendly User Interactions in Internet WEB Environmentfor friendly User Interactions in Internet WEB Environment

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ContentsContentsContentsContents

Commercial e-solutions: search, QA, CRM

Natural Language Processing Technology

Information Retrieval Technology

Intelligent QA solutions

Conclusions


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Conventional search engine Conventional search engine Conventional search engine Conventional search engine

Directory based Yahoo: everything

AOL search: web+AOL contents

Directhit: click monitoring for popular site top ranking

Looksmart: human compiled web site directory

Search based Altavista: you know

Excite: you know

Lycos: from search directory service

Fastsearch: first time 0.2 billion web page indexing

Inktomi: highly scalable indexing system

Google: link analysis (high precision)

Current trends: directory+ search integration


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Recent NL search and QA systemsRecent NL search and QA systemsRecent NL search and QA systemsRecent NL search and QA systems

Internet search with natural language and intelligence askjeeves: horizontal question-answering

Northernlight: natural language and phrasal search (clustering)

Empas: korean natural language search (?)

Lexiquest: lexipacks: ontology/dictionary for specific domain (context search)

Oingo: meaning oriented search (big ontology)

Natural language question answering Neuromedia (nativeminds): chatter bot (Eliza technology)

Easyask: data-base question answering

Brightware: web, email question answering (faq finding), recommendation

inquizit technology: natural language semantic analysis (concept engine)

YY-software: automatic email answering

Answerlogic: wordnet based question-answering

Answers.com: faq finding


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Interaction with customers for e-businessInteraction with customers for e-businessInteraction with customers for e-businessInteraction with customers for e-business

Internet users over 130m up to 350m by 2003 (eMarketer)

Internet commerce $1.3trillion by 2003 (Forrester research)

From e-commerce to e-business

Time

E-business

sophistication

contents

Transactions

communicationsIntelligent CRM

Customer historyPurchase likelihood

Staffing requirementsPrior information history

Corporate policy about serviceetc


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Customer interaction channelCustomer interaction channelCustomer interaction channelCustomer interaction channel


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CRM architecture – 3 different viewsCRM architecture – 3 different viewsCRM architecture – 3 different viewsCRM architecture – 3 different views

Integration of data warehousing & data mining, web call-center, automatic sales and marketing

Web-enabled

Operational

Analytical Collaborative

• Sales force automation• Marketing Automation• Field Service Automation• Customer Service/Support

• Data Warehouse• Data Mart• Marketing Automation• Data Marketing

• Voice(IVR,CTI,ACD)• e-Mail• Fax/Direct Mail• Web Site

Source: META Group, June 1999


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World wide CRM marketWorld wide CRM marketWorld wide CRM marketWorld wide CRM market

• Application License

$8.3 billion

• Implementation

$5.2 billion

• SW Maintenance

$3.2 billion

Year 2003 CRM Year 2003 CRM marketmarket


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Call Center

Inbound Calls Outbound Calls

Contact CenterFax

WWW / EmailTelephone

Kiosk

Sales Force AutomationDirect Mail

Call Center solutions: integration of media


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Conclusions


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NLP technology: eliza scriptingNLP technology: eliza scriptingNLP technology: eliza scriptingNLP technology: eliza scripting

<heading-0> "Rule Heading"

a:0.2 the rule activation level

p:35 *what*keyword* the pattern priority and word pattern

r:robot's reply

<work-0>

a:0.5

p:60 Wh *your*job*

r:I’m a full time Verbot

<leasure-2>

a:0.4

p:30 What time * your * job over.

r:I don’t get any time off, I always have to be here available for you.


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POS tagging (with morpheme analysis)POS tagging (with morpheme analysis)POS tagging (with morpheme analysis)POS tagging (with morpheme analysis)

포항공대 이근배 교수님께서 신을 신고 신고하러 가신다 .[ 0,0 ( 0,0 )] 등 1.000000e+00(1.000000e+00) s< 문장시작 >([)[ 1,10( 1,1 )] 미 8.288423e-11(6.102822e-13) MPO< 포항공대 >( 포항공대 )[11,11( 2,2 )] 등 8.736421e-02(2.559207e-20) s<#>(#)[12,18( 3,3 )] 미 9.236515e-08(7.008548e-24) MPN< 이근배 >( 이근배 )[19,19( 4,4 )] 등 8.736421e-02(2.939022e-31) s<#>(#)[20,23( 5,5 )] 등 4.469725e+00(1.564634e-25) MC< 교수 >( 교수 )[24,26( 6,6 )] 등 1.373613e+02(1.504397e-25) -< 님 >( 님 )[27,30( 7,7 )] 등 1.307859e+01(1.831031e-25) jC< 이 >( 께서 )[31,31( 8,8 )] 등 8.736421e-02(7.678394e-33) s<#>(#)[32,34( 9,9 )] 등 3.250709e+00(3.667919e-27) MC< 신 >( 신 )[35,37(10,10)] 등 1.264760e+01(3.865534e-27) jC< 을 >( 을 )[38,38(11,11)] 등 8.736421e-02(1.621005e-34) s<#>(#)[39,41(12,12)] 등 5.807344e+00(1.021970e-28) DR< 신 >( 신 )[42,43(13,13)] 등 3.936314e+01(1.918250e-28) eCC< 고 >( 고 )[44,44(14,14)] 등 8.736421e-02(8.044147e-36) s<#>(#)[45,49(15,15)] 등 8.588220e-04(1.297090e-33) MC< 신고 >( 신고 )[50,51(16,16)] 등 2.626376e+01(1.404345e-33) y< 하 >( 하 )[52,56(17,19)] 등 1.445488e+03(1.043073e-31) eCC< 러 >( 러 )[52,56(17,19)] 등 1.445488e+03(1.043073e-31) s<#>(#)[52,56(17,19)] 등 1.445488e+03(1.043073e-31) DI< 가 >( 가 )[57,58(20,20)] 등 4.657808e+01(1.348953e-31) eGS< 시 >( 시 )[59,61(21,21)] 등 1.841659e+01(4.754894e-31) eGE< 는다 >( ㄴ다 )[62,64(22,22)] 등 1.250000e-07(1.365400e-38) s.<.>(.)[65,65(23,23)] 등 2.500000e-05(1.638481e-49) s< 문장끝 >(])


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NLP technology: postag exampleNLP technology: postag exampleNLP technology: postag exampleNLP technology: postag example

POSTAG architecturePOSTAG architecture

Features Statistics+rule combination

Tight coupling with morpheme analysis

Morpheme graph representation

Pattern dictionary concepts for unknown

words

100,000 morpheme dic.

1,500 morpheme pattern dic.

Morpheme dicMorpheme dic

Morpheme pattern dicMorpheme pattern dicMorph.Morph.

AnalyzerAnalyzer

Morph.Morph.AnalyzerAnalyzer

Morphemegraph

POS taggPOS taggerer

POS taggPOS taggerer

Morph Morph adjacency tableadjacency table

POS BigramPOS Bigram

Syllable TrigramSyllable Trigram

Input sentence

ErrorErrorcorrectercorrecter

ErrorErrorcorrectercorrecter

ErrorErrorCorrection rulesCorrection rules

Error corrected Morpheme

graph

Parser, applicatiParser, applicationon

Parser, applicatiParser, applicationon


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Unknown word guessingUnknown word guessing Unknown word guessingUnknown word guessing

morpheme pattern dic

Syllable constraints for each part of speec

h in Korean

Lexical probabilities for unknown words Syllable tri-gram equations

morpheme anlaysis with unknown word guessingmorpheme anlaysis with unknown word guessing

POS<lemma> (allomorph) [connect info.]

HIㅂ<ZV*ZVㅂ> (ZV*워) [축약>]

DIㅅ<ZV*ZV젓> (ZV*젓) [규>]

HIㅂ<ZV*ZVㅂ> (ZV*워) [축약>]

Pattern dic for unknown wordsPattern dic for unknown words

),|Pr(#),|Pr( 만종종박만

)Pr(

)|Pr(

MPN

MPN 박종만

),|#Pr()#,|#Pr( 박종박

Morpheme dicMorpheme dic

Morpheme pattern dicMorpheme pattern dic



Morph Morph adjacency tableadjacency table

Input sentence

FilterFilterFilterFilter Filtering info.Filtering info.

Filtered Morpheme

graph

POS taggerPOS taggerPOS taggerPOS tagger


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Syntactic parsingSyntactic parsingSyntactic parsingSyntactic parsing

/complete/end---s< 문장시작 >([)complete \ /np---MCC< 엄마 >( 엄마 ) \ /v/(v\np[j 가 ]) \ / \(v/(v\np[j 가 ]))\np---jC< 이 >( 가 ) \ /v[D] \ / \ /np---TCH< 아이 >( 애 ) \ / \ /v/(v\np[j 에게 ]) \ / \ / \(v/(v\np[j 에게 ]))\np---jC< 에게 >( 에게 ) \ / \v[D]\{np[j 가 ]} \ / \ /np---MCC< 심부름 >( 심부름 ) \ / \ /v/(v\np[j 를 ]) \ / \ / \(v/(v\np[j 를 ]))\np---jC< 을 >( 을 ) \ / \v[D]\{np[j 가 ],np[j 에게 ]} \ / \v[D]\{np[j 가 ],np[j 를 ],np[j 에게 ]}---DR< 시키 >(시켜 ) \ /vp[ 었 ] \ / \vp[ 었 ]\v---eGSt< 었 >( ㅆ ) \ /s[ 서술 ] \ / \s[ 서술 ]\vp---eGEs< 다 >( 다 ) \ /s[ 서술 ] \ / \s\s---s.<.>(.) \end \end\X---s< 문장끝 >(])

엄마가 아이에게 심부름을 시켰다


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Syntactic parsing: pospar exampleSyntactic parsing: pospar exampleSyntactic parsing: pospar exampleSyntactic parsing: pospar example

Functional applicationX/(Args {Y}) Y X/ArgsY X\(Args {Y}) X\Args

CompositionX/(X\ArgsX) Y/(Y\ArgsY)

X/(X\(ArgsX ArgsY))Y\ArgsY X\(ArgsX {Y})

X/(X\(ArgsX ArgsY))

CoordinationX CONJ X X

Variable category$v, $vp

Featured categoryv : D, H, I, Evp : 었 , 었었 , 고있 , 어있 , 겠 , 더 , 시s : 평서 , 의문 , 명령 , 청유 , 약속 , 문장np : j 이 , j 를 , j 에게

morpheme category Syntactic dic.

DIㄹ <날 >[*] v \{np[j가 ]} DI여 <하 >[*] v \{np[j가 ], np[j를 ]} eGEm<어 라 >[*] s[명 령 ] \v

Syntactic pattern dic MC*<*>[*] n MC*<*>[*] np

Syntax dicSyntax dic

Syntax pattern dicSyntax pattern dic

Parse tree

Syntactic Syntactic AnalyzerAnalyzer

Syntactic Syntactic AnalyzerAnalyzer

Syntactic CateSyntactic Category Trigramgory Trigram

Korean CCGKorean CCG

Morpheme graph

Semantic Semantic AnalyzerAnalyzer

Semantic Semantic AnalyzerAnalyzer

Syntactic dic. and Syntactic pattern dic.Syntactic dic. and Syntactic pattern dic.

POSPAR architecturePOSPAR architecture


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Semantic analysis: exampleSemantic analysis: exampleSemantic analysis: exampleSemantic analysis: example

자연어처리를 전공한 교수가 가르치는 과목은 ?(What is the course name that a professor whose major is NLP teaches?)

--------------------------- Semantic Result --------------------------

Scope: [0, 17][ques, [contra, term(<quant,bare,sing>,X7, [and, [course,X7], [teach,EV3, term(<def,bare,sing>,X6, [and, [professor,X6], [major,EV1,X6, term(<quant,bare,sing>,X1, [NLP,X1])]]),X7,\_:p[j 에 ];0F]])]]


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Semantic analysis: system overviewSemantic analysis: system overviewSemantic analysis: system overviewSemantic analysis: system overview

Morphological Analyzer

QLF StructuresSyntactic Trees

Semantic AnalyzerPOS Tagger

Semantic Dictionaries(base/dom/pat/user/rel)

Thesauri

Semantic-based ApplicationsSemantic-based Applications

Slot-Filler Generator

Input Sentence

K-CCG Parser

Topic/Subject Extractor...… ...…


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NLP technology: Korean WordNetNLP technology: Korean WordNetNLP technology: Korean WordNetNLP technology: Korean WordNet

Map Korean words to other existing thesaurus (WordNet)

Using bi-lingual dictionary

Automatic mapping tools using WSD techniques

Korean word English word WordNet synset

kwi_j

ew1

ws1

ewm

…

ws2

wsk

wsn

… …

…


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NLP technology: Korean WordNetNLP technology: Korean WordNetNLP technology: Korean WordNetNLP technology: Korean WordNet

Multiple heuristics for WSD

Maximum similarity

Prior probability

Sense ordering

IS-A relation

Word match

Cooccurrence

Combining heuristics with machine leaning techniques

Decision tree

Logistic regression


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Conclusions


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Several gaps in the searchSeveral gaps in the searchSeveral gaps in the searchSeveral gaps in the search

Search engineSearch engine

tasktask

Info needInfo need

Verbal formVerbal form

queryquery

resultsresults

Query refinementQuery refinement

webweb

Mis-conceptionMis-conception

Mis-translationMis-translation

Mis-formulationMis-formulation

Polysemy/synonymyPolysemy/synonymy

interactiveinteractive

QA (askjeeve)QA (askjeeve)

Nlp query Nlp query

(easyask, lexiquest)(easyask, lexiquest)

Queries in context Queries in context (domain)(domain)

(autonomy, verity)(autonomy, verity)

Clustering (northernlight)Clustering (northernlight)


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Web search vs classical IRWeb search vs classical IRWeb search vs classical IRWeb search vs classical IR

Classical IR

Fixed document corpus

Document relevancy is the goal

Contexts (domain) and individual users (preferences) ignored

Web search

Public web: static + dynamic (generated from RDB)

High quality ranking is the goal (meet the user need given poor query and heterogeneity

of the web)

Various needs such as informational, navigational, transactional


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Search engine techniquesSearch engine techniquesSearch engine techniquesSearch engine techniques

First generation TF/IDF from standard IR

Use only page data (text data)

Html parsing for weighting

Second generation Use off-page and web specific data

Such as link (connectivity) analysis, click-through data (relevance feedback), anchor-

text data

Third generation Answer the need behind the query

Semantic analysis, context determination, dynamic corpus from RDB, validity

(authority), cross-lingual/cross-media, question-answering, specific enterprise site

search, etc


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ⓝⓝ constructing high-quality corpus: Web constructing high-quality corpus: Web ROBOTROBOTⓝⓝ constructing high-quality corpus: Web constructing high-quality corpus: Web ROBOTROBOT

Web-page Filtering

• URL Domain Filtering• File Type Filtering• URL Name Filtering

11st st trying trying TargetTarget

Web DocumentWeb DocumentFiltered Target URL

File info.(Date, Size, Link)

File Collection & Management

• manage independent

site • saved by URL Hierarchy• make Log Files

DomainSite 1

Smart Updating

• save new Web-page• overwrite updated-page

Saved File

Collected File.

Result File Pool

WEBtagger

Result File Manager

WEBtagger

Result File Manager

DomainSite 2

Updating sameUpdating sameWeb DocumentWeb Document

Various User-Input Option Various User-Input Option ::Filtering ConstraintsFiltering Constraints


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High quality corups: WEB preprocessingHigh quality corups: WEB preprocessingHigh quality corups: WEB preprocessingHigh quality corups: WEB preprocessing

Result Result File PoolFile Pool

• Entry Dic, Pattern DicEntry Dic, Pattern Dic• Postag Noun Trie Dic.Postag Noun Trie Dic.• C4.5 RuleC4.5 Rule

HTML Refiner HTML Refiner

Sentence ExtractorSentence Extractor

Word Spacing Corrector

Word Spacing Corrector

Result File ManagerResult File Manager

nROBOT (Web Crawler)

nROBOT (Web Crawler)

• Regexp Patterns RuleRegexp Patterns Rule• Heuristic RuleHeuristic Rule• Abbreviation DicAbbreviation Dic• Symbol-Delimiter DBSymbol-Delimiter DB• C4.5 RuleC4.5 Rule

• Tag Corrector & ParserTag Corrector & Parser• Garbage String FilterGarbage String Filter

Web documentWeb documentInputInput

POSTAGPOSTAG TTS SystemTTS SystemSAASAA

XML DOC.Form A Form B

POSNIRPOSNIR

XML DOC. Form D Form C


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High quality corpus: Automatic indexingHigh quality corpus: Automatic indexingHigh quality corpus: Automatic indexingHigh quality corpus: Automatic indexing

Morpheme Analysis

POS Tagging

Term Extraction

Term weighting

Documents

index DB

Indexing architecture Based on general morpheme tagging

Term Extraction

nominals - single terms compound noun generation

– using rules automatically learned– filtering through precision (preventing over-gener

ation)

compound noun segmentation– based on mutual information

Term weighting

for document ranking based on TF, IDF measures


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Compound nouns in indexingCompound nouns in indexingCompound nouns in indexingCompound nouns in indexing

POSNIR features Noun extraction

ex) 철수는 회의에서 그 사건을 보고할지도 모른다 . (Chulsoo may report the ac

cident at the meeting) bogo(O)

ex) 지도를 보고 길을 찾는다 . (see a map and find a load) bogo(X) Compound noun segmentation

Compound noun patterns plus statistical collocation (mutual informa

tion)

ex) 대학생선교회 (undergraduate missionary) 대학생 / 선교회 (O), 대학 (un

iversity)/ 생선 (fish)/ 교회 (church) (X) Compound noun indexing (phrasal indexing)

Using automatically acquired extraction rules

Broad coverage of compound noun pattern recognition

ex) 증기로 움직이는 기관차 (locomotive operating by steam) 증기 (

steam)/ 기관차 (locomotive)


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Dealing with user queries: NL queryDealing with user queries: NL queryDealing with user queries: NL queryDealing with user queries: NL query

NLP Engine

MorphemeAnalysis

Tagging

Query Term Extractionand

Boolean FormulationDBDB

Boolean Operationand Ranking

DB Search

NL QuerySearh Result

Tagged

Sequence


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Humans extracts meaning in many linguistic levels Humans extracts meaning in many linguistic levels but current web search is only counting words – Is but current web search is only counting words – Is it enough?it enough?

Humans extracts meaning in many linguistic levels Humans extracts meaning in many linguistic levels but current web search is only counting words – Is but current web search is only counting words – Is it enough?it enough?

Part of words – morpheme

Word order

Word lexicals

Text structure or document structure

Clue words/cue phrase

Pronunciation/prosody

World knowledge


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NLP helps high-precision web searchNLP helps high-precision web searchNLP helps high-precision web searchNLP helps high-precision web search

Information retrieval dilemma

Hard to ask right questions

Too much information

Irrelevant information

No information (phrase mismatch)

NLP tools to help avoiding information dilemma

Context of words: collocations

Syntax cues:how word is used

Concept mapping with clustering

Interactivity by clarifying dialog


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Other Related Other Related TechnologyTechnologyOther Related Other Related TechnologyTechnology

IR Application

XML & KM

MachineLearning

Q/A Applicatio

n

Domain Ontology Mgmt ToolDomain Ontology Mgmt Tool

Contents Auto-BuilderContents Auto-Builder

Text PreprocessorText Preprocessor

Intelligent Web RobotIntelligent Web Robot

Text SummarizerText Summarizer

Text CategorizerText Categorizer

Document CategorizationDocument Categorization

Similar Text ClusteringSimilar Text Clustering

Information ExtractionInformation Extraction

Wrapper InductionWrapper Induction

K-Wordnet Auto-Builder K-Wordnet Auto-Builder

Answer SuggesterAnswer Suggester

Multi-Lingual IR EngineMulti-Lingual IR Engine Fuzzy-SQL GeneratorFuzzy-SQL Generator

Shopping Aid Agent SolutionShopping Aid Agent Solution

NL-Query AnalyzerNL-Query Analyzer

FAQ Finder SolutionFAQ Finder Solution

Korean NLP Core Engine

POS-TaggingPOS-Tagging

Syntactic AnalysisSyntactic Analysis

Semantic-Discourse AnalysisSemantic-Discourse Analysis DBQ/A SolutionDBQ/A Solution

Comp-Noun AnalyzerComp-Noun Analyzer


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Conclusions


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The third generation search engineThe third generation search engineThe third generation search engineThe third generation search engine

Natural language question-answering : Answer-providing for dialog

questions

Answer sentence extraction (DiQuest d-Answer) Pre-defined question types Semantic-level processing of NL query

Answer finding from FAQ (DiQuest e-Answer) Systematic construction of FAQ Finding semantically same questions from FAQ list Email/Web call center applications

Answer finding from R-Database (DiQuest db-Answer) Finding answers from R-DB attributes SQL conversion from natural language query

Companies Neuromedia, Answerfriend, Answers.com, Brightware, Answerlogic, Easyask, etc.


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Easy Interface with Natural DialoguesEasy Interface with Natural DialoguesEasy Interface with Natural DialoguesEasy Interface with Natural Dialogues

DiQuest Q/A : Total dialog information retrieval solutions Easy and accurate information retrieval using natural language dialog

Retrieval from any information source including internet/intranet web documents, FAQ knowledge, data

bases

DiQuest

Q/A Solution

DiQuest

d-Answer

DiQuest

e-Answer

DiQuest

db-Answer

Other NLP

Applications


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Why Dialog Web Interface?Why Dialog Web Interface?Why Dialog Web Interface?Why Dialog Web Interface?

Efficiency: no need for web surfing Accuracy: exact description of search Convenience: using everyday dialog sentences Customer satisfaction guaranteed!

Easy to catch customers’ needs in natural language query (Not easy to catch customers’ needs using only keywords query) Customer-oriented Web content management Customer–oriented FAQ K/B construction and maintenance Personal profile management for each customer (CRM)

Customer-Side

Company-Side


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Spectrum of ProductsSpectrum of ProductsSpectrum of ProductsSpectrum of Products

CRM/KM/E-commerce Information retrieval Document processing Language processing

shopping mall

retrieval

Wireless question

answering

FAQ finding

NL-SQL conversion

Answer indexing

Complex term

indexing

Morphology

Syntax

Email/web call center

Intranet question

answeringVertical IR

Answer sentence extraction

Question type

processing

Structure indexing

Semantics Dialogs

ServiceService PackagePackage ComponentComponent LibraryLibrary


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Branded ProductsBranded ProductsBranded ProductsBranded Products

BrandBrand propertiesproperties performanceperformance applicationsapplications

dAnswerdAnswer

eAnswereAnswer

dbAnwerdbAnwer

• Vertical retrieval• High speed indexing• Answer sentence extraction• Optimized retrieval

• Vertical retrieval• High speed indexing• Answer sentence extraction• Optimized retrieval

• Answer finding from FAQ knowledge base• Real time FAQ construction/indexing• Possible fusion with d-Answer/ db-Answer

• Answer finding from FAQ knowledge base• Real time FAQ construction/indexing• Possible fusion with d-Answer/ db-Answer

• SQL feature computation• Automatic vocabulary construction• Optimized for given RDB schema

• SQL feature computation• Automatic vocabulary construction• Optimized for given RDB schema

• 0.1 million doc. answer sentence extraction (about 1sec response)• 1 million doc vertical IR 1 million doc vertical IR (about 0.3 sec)(about 0.3 sec)• platform: Linux, Solaris, HPUX

• 0.1 million doc. answer sentence extraction (about 1sec response)• 1 million doc vertical IR 1 million doc vertical IR (about 0.3 sec)(about 0.3 sec)• platform: Linux, Solaris, HPUX

• Over 10,000 FAQ doc.Over 10,000 FAQ doc. (about 0.3 sec response)(about 0.3 sec response)• More than 1000 simultaneous access• platform: Linux, Solaris

• Over 10,000 FAQ doc.Over 10,000 FAQ doc. (about 0.3 sec response)(about 0.3 sec response)• More than 1000 simultaneous access• platform: Linux, Solaris

•100% retrieval accuracy100% retrieval accuracy• Over 100,000 records (0.3 sec response)• platform: Linux, Solaris

•100% retrieval accuracy100% retrieval accuracy• Over 100,000 records (0.3 sec response)• platform: Linux, Solaris

• Document search for KM/internet/portals• Answer finding for KM/intranet• High precision search for wireless application

• Document search for KM/internet/portals• Answer finding for KM/intranet• High precision search for wireless application

• Email call center• Web call center• Automatic FAQ knowledge base construction• CRM analysis

• Email call center• Web call center• Automatic FAQ knowledge base construction• CRM analysis

• Product search for e- commerce (B2B, B2C, B2G)• Employ portal/business portal• Intranet/KM DB search

• Product search for e- commerce (B2B, B2C, B2G)• Employ portal/business portal• Intranet/KM DB search

competitorscompetitors

• Verity• Askjeeves• Verity• Askjeeves

• Brightware• Egains• Brightware• Egains

• Easyask• ELF/Microsoft• Easyask• ELF/Microsoft


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DiDiQQuest uest dd-Answer-Answer: Vertical IR agent with answer extraction: Vertical IR agent with answer extractionDiDiQQuest uest dd-Answer-Answer: Vertical IR agent with answer extraction: Vertical IR agent with answer extraction

High precision optimizable IR engine Horizontal IR limitations : focusing high speed indexing, sacrificing high precision Why Vertical IR?

User intention analysis using language processing

Optimization possible for specific domain/portal

Intelligent IR engine for answer sentence extraction Conventional natural language IR (e.g. askjeeves) limitations

Only provide documents which possibly include query terms

It is the USER who needs to find exact information in the documents Why Q/A System ?

Provide direct answers (information) rather than thousand of documents

Towards true meaning of information retrieval (next generation IR)


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DiQuest

d-Answer

Result

File & Answer

“R 관”“ 공학관 7 층”

Query Analysis

DiDiQQuest uest dd-Answer-AnswerDiDiQQuest uest dd-Answer-Answer

DiQuest d-Answer Merits Spectrum of solutions from high precision IR to intelligent question-answering

system with natural language dialog query Web site question answering engine: extract sentences that contain possible

answers as well as documents for users’ questions

DiQuest d-Answer : Question Example

“ 삼성그룹 회장은 ?” (Who is the chairman of Samsung group?)

“ 야후코리아의 홈페이지 주소와 김경희 팀장의 이메일은 ?”

“ 야후코리아의 사장은 누구인가”

“ 윈도우 미의 가격 ?” (What is the price of Windows ME?)

“ 물건 반납에 관한 것을 상담하려면 어디에 전화해야 하나요 ?”

“ 화공과는 어디에 있나요” (Where is the CE dept.?)


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DiDiQQuest uest dd-Answer Preview-Answer PreviewDiDiQQuest uest dd-Answer Preview-Answer Preview

Answer Suggestions


43

DiQuest SiteQ – Natural Language Answer Extraction

System Architecture


44

DiDiQQuest uest ee-Answer-AnswerDiDiQQuest uest ee-Answer-Answer

FAQ finding engine FAQ : frequently asked question knowledge-base (question/answer pairs)

80% of user questions can be processed using well constructed FAQ lists

Automatically finding optimized answers from FAQ lists

Reducing email/phone calls using automatic FAQ finding solutions (customer satisfaction increased)

Finding semantically same questions from FAQ knowledge-base

Exact pin-pointing of users’ question intentions

Structural analysis of sentences for finding same-meaning questions

Highly precise retrieval using specialized analysis for question and answer parts in faq KB

Conventional keyword IR techniques cannot retrieve semantically same questions !!

Intelligent answering agent with FAQ


45

DiDiQQuest uest ee-Answer Preview -Answer Preview (1)(1)DiDiQQuest uest ee-Answer Preview -Answer Preview (1)(1)

Answer Suggestion


46

DiDiQQuest uest ee-Answer Preview -Answer Preview (2)(2)DiDiQQuest uest ee-Answer Preview -Answer Preview (2)(2)

e-Answer combined with d-Answer

Answer Suggestion


47

System Architecture

DiQuest FAQ Finder


48

DiDiQQuest uest DBDB-Answer-AnswerDiDiQQuest uest DBDB-Answer-Answer

Database search engine information retrieval in the relational database (using SQL computation)

automatic term indexing by analyzing running database

Translate users natural language questions into standard SQL for relational database computing

Recursive natural language query (automatic query refinement)

Fusion solutions with e-Answer and d-Answer

Integrated search for product description texts with product database

Integrated search for web documents with highly variable data in structured database

Intelligent RDB search Engine


49

DiDiQQuest uest DBDB-Answer Preview-Answer Preview(1)(1)DiDiQQuest uest DBDB-Answer Preview-Answer Preview(1)(1)

자연어 질의 분석 후 SQL 생성


50

DiDiQQuest uest DBDB-Answer Preview-Answer Preview(2)(2)DiDiQQuest uest DBDB-Answer Preview-Answer Preview(2)(2)

이전 결과에 대한 담화 (Discourse) 유지


51

DiQuest DBQ – Natural Language SQL Interface

System Architecture


52

Web Total Web Total Q/AQ/A System Architecture System ArchitectureWeb Total Web Total Q/AQ/A System Architecture System Architecture


53

E-commerce applicationsE-commerce applicationsSAA (Shopping Aid Agent) – web mining back-end SAA (Shopping Aid Agent) – web mining back-end solutionsolution

E-commerce applicationsE-commerce applicationsSAA (Shopping Aid Agent) – web mining back-end SAA (Shopping Aid Agent) – web mining back-end solutionsolution

Web robots Category specific web crawling (remove duplicates)

Categorizer Categorize the web documents into the pre-defined domain classes

Extractor Web information extraction to build R-db extraction using mDTD (modified Document Type Definition) Sequential mDTD learning to generate new mDTD rules

Natural Language query to automatically constructed RDB

Comparison-based shopping, automatic job search, continued-educ

ation

Whizbanglabs.com (from CMU)


54

E-commerce SAAE-commerce SAAE-commerce SAAE-commerce SAA

SGML Documents

DTD

DocType Definition

Analysis & Encoding

Training Documents(structured HTML)

mDTD

Learning Extraction

Web Documents(structured and

semi-structured Documents)

Basic Idea


55

E-commerce SAAE-commerce SAAE-commerce SAAE-commerce SAA

ExampleExtraction

mDTDParsing

Learned mDTD

Structured Documents

Seed mDTDs

SequentialLearning

Domain DB for

AV

ExtractionSlot Filling

DB building

Sequential mDTD Learner

Extractor

Web Robot

Seed URLs

HTMLGathering

Categorizer

mDTDParsing

Learned mDTD

Structured /Semi-structured Documents

HTMLDocument

s

knnBi-categorizing

DomainDocument

s


56






Conclusions


57

NLP QA/vertical search applicationsNLP QA/vertical search applicationsNLP QA/vertical search applicationsNLP QA/vertical search applications

Internet/intranet vertical retrieval

eCRM/web-based CRM (automated call center)

Comparison based e-shopping mall/meta mall

WAP enabled PDA/cell phone retrieval

KMS embedded solutions

Voice enabled retrieval/ voice portal retrieval


58

Future perspectivesFuture perspectivesFuture perspectivesFuture perspectives

Long term future Apple’s bow tied man -- new millenium dream

SF films -- “angel” in “disclosure” movie

HAL in space odyssey 2001 (forever dream?)

Short term future General magic’s portico system (http://www.genmagic.com/portico/portico_home.shtml)

Microsoft persona project -- peedy (http://msdn.microsoft.com/workshop/c-frame.htm#/

workshop/imedia/agent/default.asp)

Diquest.com – total QA solution (www.diquest.com demo)

Date post:	14-Dec-2015
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