entity and aspect extraction for organizing news comments

Entity and Aspect Extractionfor Organizing News Comments

Radityo Eko Prasojo, Mouna Kacimi & Werner NuttMelbourne 19–23 October 2015

Comments in News WebsiteTypically, commentsarelistedbasedondate-timeandreplyrelation.

Problem:difficultytocatchtheflowofthediscussionsandtounderstandtheirmainpointsofagreementanddisagreement.

Example:whyisindependencegood/bad fortheScots?Willtheireconomy beaffected?

CIKM2015 Prasojo,Kacimi,&Nutt- EntityandAspectExtractionforOrganizingNewsComments

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There is a need for organizing comments

to help users to:(1) have a better understandingof the viewpoints related to each topic(2) facilitate the participation in discussions and thus increase the

chance of acquiring new viewpoints

by clustering comments containing similar discussions:

• they talk about the same entities

• they argue about the same aspects of those entities.CIKM2015 Prasojo,Kacimi,&Nutt- EntityandAspectExtractionfor

OrganizingNewsComments3

Contributions• Improvementsonstate-of-the-artunsupervisedentityextractiontools(Zemanta,NERD,AIDAYago)• Addressedissues:noisesandlowcoverage(duetocoreferences)

• Introduced aspectextractioninnewsdomain• Previously:aspectsonlyonproductreviewdomain(Zhang&Liu,2014)


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An entity can be…

aperson,alocation,anorganization,oranywell-definedconcept suchasnationalities,languages,orwars.


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Entity Extraction Tasks1. Recognition– throughpropernames/rigiddesignators

(Coates-Stephens,1992)(Thielen,1995)(Nadeau&Sekine,2006)

2. Disambiguation – bymappingtoarepresentationinaKB


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“Scotland can vote however it wants, it's the Scottish peoples right.”

“If he ends up sending USS Ronald Reagan aircraft carrier to the coast of Scotland, then he should have done the same to Crimea.”

<http://dbpedia.org/resource/USS_Ronald_Reagan_(CVN-76)>

<http://dbpedia.org/resource/Crimea><http://dbpedia.org/resource/Scotland>

Supervised vs Unsupervised Approachesto Entity Extraction

AspectofEE

Prominent tools

Recognitionability

Disambiguation ability

Running time

Supervised

StanfordNLP

dependsonthetrainingset

limited

fast

UnsupervisedZemanta,AlchemyAPI,NERD,AidaYAGO

domain-independent

providedbyKBs

slow


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Entity extraction Baseline: Unsupervised Tools

• Improvedbyapplying:• Entityfiltering• Namenormalization• EntitysearchonKB• Coreference resolution


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Tools:DbpediaStanfordCoreNLP

“Don't be afraid of Rasmussen or NATO, this is none of their business. If he ends up sending USS Ronald Reagan aircraft carrier to the coast of Scotland, then he should have done the same to Crimea.”

<http://dbpedia.org/resource/NATO>

<http://dbpedia.org/resource/Scotland><http://dbpedia.org/resource/USS_Ronald_Reagan>

“Don't be afraid of Rasmussen or NATO,this is none of their business. If he ends up sending USS Ronald Reagan aircraft carrier to the coast of Scotland, then he should have done the same to Crimea.”

<http://dbpedia.org/resource/Crimea><http://dbpedia.org/resource/Aircraft_Carrier>



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rdf:type ofNATO?à←dbo:PopulatedPlace

Anentity isaninstanceofsomewell-definedclass

rdf:type?à←dbo:Ship

rdf:type?à←dbo:Location

rdf:type?à←owl:Thing

Entity Filtering



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Weremoveentities thatdon’thaverdf:type otherthanowl:Thing andowl:Class

rdf:type ofNATO?à←dbo:PopulatedPlace

Anentity isaninstanceofsomewell-definedclass

rdf:type?à←dbo:Ship

rdf:type?à←dbo:Location

Risk:lowerrecall

rdf:type?à←owl:Thing

Entity Filtering


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Anentitymayappearusingnon-propernames(alias)


Lorem ips um dolor s it amet, consecte turadipisc ing el i t. Ut s usc ipi t lacus ac bibendum mol lis. Pe llenteas dsadas quetinci tUnitedStates

Lorem ips um dolor s it amet, consecte turadipisc ing el i t. Ut s usc ipi t lacus ac bibendum mol lis. Pe llentesquetinc idunt vu lputate ligula a e ffic itur. Aliquam era tv olutpat. Al iquam sedenim et tortor

fringi l la asdasdasdasdas das dqwrqweqweqwr wqeqweAnders FoghRasmussen. Maecenas vehiculaurnaege t metus imperdie tcomm odo.

ScotlandRejectsIndependenceinReferendumLorem ipsumdolorsitamet,consectetur adipiscing elit.Ut suscipit lacusacbibendum mollis.Pellentesque tincidunt vulputate ligulaaefficitur.Aliquam erat

…………foaf:givenName,foaf:surname?à

←{‘Anders’, ‘Fogh’, ‘Rasmussen’}

dbpedia-owl:wikiPageRedirects of?à←{‘US’,‘USA’, ‘U.S.’,‘U.S.A.’,…}

Name Normalization


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Anentitymayappearusingnon-propernames(alias)


Lorem ips um dolor s it amet, consecte turadipisc ing el i t. Ut s usc ipi t lacus ac bibendum mol lis. Pe llenteas dsadas quetinci tUnitedStates

Lorem ips um dolor s it amet, consecte turadipisc ing el i t. Ut s usc ipi t lacus ac bibendum mol lis. Pe llentesquetinc idunt vu lputate ligula a e ffic itur. Aliquam era tv olutpat. Al iquam sedenim et tortor

fringi l la asdasdasdasdas das dqwrqweqweqwr wqeqweAnders FoghRasmussen. Maecenas vehiculaurnaege t metus imperdie tcomm odo.

ScotlandRejectsIndependenceinReferendumLorem ipsumdolorsitamet,consectetur adipiscing elit.Ut suscipit lacusacbibendum mollis.Pellentesque tincidunt vulputate ligulaaefficitur.Aliquam erat

…………foaf:givenName,foaf:surname?à

←{‘Anders’, ‘Fogh’, ‘Rasmussen’}

dbpedia-owl:wikiPageRedirects of?à←{‘US’,‘USA’, ‘U.S.’,‘U.S.A.’,…}

Risk:lowerprecision

Name Normalization


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Sometimes,mappingforanaliascannotbefound

AllrelatedentitiesofNATOandtheiraliasesà

←dbprop:leaderNamedbpedia:Anders_Fogh_Rasmussen,{‘AndersFogh’,‘Rasmussen’}

Stringcomparison“Don't be afraid of Rasmussen or NATO,

Context-Related Entity Search


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Sometimes,mappingforanaliascannotbefound

AllrelatedentitiesofNATOandtheiraliasesà

←dbprop:leaderNamedbpedia:Anders_Fogh_Rasmussen,{‘AndersFogh’,‘Rasmussen’}

Stringcomparison

Risk:lowerprecision

“Don't be afraid of Rasmussen or NATO,

Context-Related Entity Search


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StanfordCoreNLP

Coreference Resolution


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StanfordCoreNLP

Risk:lowerprecision

Coreference resolution

Coreference Resolution

Entity Extraction – Experiment Setup• 10newsarticlesthatuseDISQUS• 100commentshavingthehighestwordcounts• 5studentsasentity andaspect annotators• Annotateddataasgroundtruth


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Entity Extraction – Experiment Results


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Aspects• ofproductentities• Theaspectsofanentitye arethecomponents andattributes ofe.(Zhang&Liu,2014)

• ofentitiesonnews• “MoreScots woulddefinitelyhavevoted nothanyes”(voting - action)• “OrlandoBloomisagoodactor”(acting - skill)• “…it’stherightthe rightofScottishPeople”(right - possession)• Other:components,attributes,andmoods

Anaspect isallwhatisarguableaboutanentityCIKM2015 Prasojo,Kacimi,&Nutt- EntityandAspectExtractionfor


Types of Aspects

• “…it’sthe rightofScottishPeople.”(explicit)

• “Tesco is large.”(implicit)

• “Scotlandisavery beautiful country.”(semi-implicit)

• “TheScotscan vote howevertheywant.” (semi-implicit)


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aspect::right

aspect::employer

aspect::beauty

aspect::voting

Extraction of Explicit Aspects:Exploiting Dependency

“…it’sthe rightofScottishPeople.”


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StanfordCoreNLP Annotation

Extractallnounphrasesthathaveannmod:of,nmod:in,nmod:on,ornmod:atrelationtowardstheentity inthesentence.

PrepositionalDependency

• Specifically,thecoreference resolutionpart


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Extraction of Explicit Aspects, Combined with Entity Extraction

“Don't be afraid of Rasmussen or NATO, this is none of their business.”

possessivedependency

Extraction of Implicit Aspects:Adjective-to-aspect MappingIdeafrom(Zhang&Liu,2014)

“Tesco islarge”

Inothercomments,wefoundasaspectsofTesco,qualifiedas“large”:• employer(2x)• backoffice(1x)• callcenteroperation(1x)

Weconclude:mostprobably,theemployer aspectofTesco wasmeant.Resultisfurtherimprovedby(1)takingintoaccountfrequentcontextwordsand(2)lexicalrelationsofadjective.


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Extraction of Semi-Implicit Aspects (1)• Semi-implicitaspects:implicitaspectsthatdon’thavemapping.• “Scotlandisavery beautiful country.”

beautiful beautyWesearchforanounphrasethatisconnectedtotheentityandthewordindicatingtheaspectusingalexicalrelation.CIKM2015 Prasojo,Kacimi,&Nutt- EntityandAspectExtractionfor


WordNet:attribute

StanfordCoreNLP Annotation

Lexicaldatabasesearch Weconsiderfollowinglexicalrelations:attribute>pertainym>participleofverb>derivationallyrelatedform(drf)>seealso

Extraction of Semi-Implicit Aspects (2)• Generallycanbeusedtoidentifyaspects fromverb,adjective,ornoun.• Otherexamples:

beautiful beautyinstrumental instrumentvote votinginexcusable excusable justifiable

justification justify• Iftherearemultiplepossibleaspects forasingleword,weuseWordNet::Similaritytodecideforthebestone.


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WordNet:attribute

WordNet:pertainym

WordNet:derivationally relatedform

WordNet:antonym WordNet:similar to

WordNet:drfWordNet:drf

Aspect Extraction –Experiment and Results


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Visualization


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REPrasojo,MKacimi,FDarari.IEEEInfoVis.Chicago,25-30October2015.

demoisnowavailableatorcaestra.inf.unibz.it

Conclusion• Generalcontribution:aframeworkfororganizingnewscomment usingentityextractionandaspectextraction.

• Ourentityextraction: unsupervisedtools+entityfiltering,namenormalization,entitysearch,andcoreference resolution.

• Weextractexplicit,implicit,andsemi-implicit aspectsusinggrammaranalysisandlexicaldatabasesearch.

• Experimentshowsimprovementonbothentity andaspect extractioncomparedtobaselinetechnique.


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Future Work• Addressinglimitations:• Conceptextractions• Idiomsandothermetaphoricalexpressions• Difficultcoreference (e.g.demonstrativepronouns)• Experimentsonmore,variousdata

• Completethemissingpieces:• Sentimentanalysisfornewscomments


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• SIGIRStudentTravelGrant

• To-KnowProject

• EuropeanMaster’sProgramme inComputationalLogic(FreeUniversityofBozen-BolzanoandTUDresden)


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Acknowledgements

entity and aspect extraction for organizing news comments

Data & Analytics