Courses 5-6 Processing the natural language Main issues...

Courses5-6Processingthenaturallanguage

Mainissuesandsolu6ons

Theresearchdomain

•  Computa(onalLinguis(cs(CL)–givesthetheore6calbackground,linguis6cmodels,computa6onaltheoriesonlanguage

•  NaturalLanguageProcessing–appliedCL– naturallanguagetechnology– humanlanguagetechnology

2

Naturallanguagetechnology

•  Spokenlanguage–speechprocessing•  WriGenlanguage•  Languageincorrela6onwithothermodali6es(mul6modality)

3

Speechtechnologies

•  Interpre6nghumanvoice–  reprezenta6onofvocalsignal– speechrecogni6on(speechtotext-S2T)– prosodyanalyses– speakerrecogni6on

•  Synthesizinghumanvoice– speechsynthesis– prosodysynthesis

4

ProcessinghumanspeechsignalPRAATinterface

ThankstoProf.H.N.Teodorescu5

WriGenlanguagetechnologies

•  Documentsegmenta6onandinterpreta6on– cleaning(ellimina6onofdots,enhancingcontrast,etc.)

– separa6onoftextfromimage,curvedlines...–  recognisingprinted,semi-uncialcharactersandhandwri6ng

•  Op(calCharacterRecogni(on(OCR)

6

Ms.45BARCluj-Napoca,secondhalfof17thcentury

Differenttypesofwri6ngintherevisedcopyofNicolaeMilescu’stransla6onofSeptuaginta,p.412-413

7

WriGenlanguagetechnologies

•  AnalysisandunderstandingofwriGenlanguage– sub-syntac6cprocessing

•  lexicalunits•  sentencespli[ng•  clauseborders•  partofspeechandmorphologicalinforma6on•  lemmas•  en6tynames•  groups(nominal,verbal,prepozi6onal,etc.)andlexicalaGrac6ons(coloca6ons)

8

SentencesTheLegalCommissionoftheChamberofDepu6esvotedMondayagainsttheini6a6onofcriminalprosecu6onofRovanaPlumb,theresigningministeroftheEuropeanFunds,a_erhearingher,alongwiththelawyer,foraboutanhourbylawmakers.|RovanaPlumbonceagaindeclaredherself,whenoutofhearingsinthelegalcommission,innocentoftheaccusa6onsmadebyan6corrup6onprosecutors.|

9

ClausesOnSeptember22,DNAannouncedthat|DeputyPrimeMinisterSevilShhaidehissuspectedof|commi[ngserviceabusewhenhewasastatesecretaryatMDRAP,inadossierthat,in2013,|throughtheconcertedac6onofsomepublicofficials,partsoftheBelinaIslandandthePavelBrassillegallypassedfromtheStatepropertytopropertyofTeleormanCountyandintheadministra6onofTeleormanCountyCouncil,|justafewdaysa_er,theyalsobeingillegallyleasedtoaprivatecompany.| 10

Lexicalunits

SolicitatsăcomentezeuneditorialrecentalluiDinuPatriciu,încareacestaprecizacănucredeînsocial-liberalismşisăapreciezedacă,asfel,adatoloviturădeimagineUSL,Antonescuaspuscănuş6edacăPatricius-areferitlaUSL.

11

Nameen66esOnSeptember22,DNAannouncedthatDeputyPrimeMinisterSevilShhaidehissuspectedofcommi[ngserviceabusewhenhewasastatesecretaryatMDRAP,inadossierthat,in2013,throughtheconcertedac6onofsomepublicofficials,partsoftheBelinaIslandandthePavelBrassillegallypassedfromtheStatepropertytopropertyofTeleormanCountyandintheadministra6onofTeleormanCountyCouncil,justafewdaysa_er,theyalsobeingillegallyleasedtoaprivatecompany.

person

12

Nameen66esOnSeptember22,DNAannouncedthatDeputyPrimeMinisterSevilShhaidehissuspectedofcommi[ngserviceabusewhenhewasastatesecretaryatMDRAP,inadossierthat,in2013,throughtheconcertedac6onofsomepublicofficials,partsoftheBelinaIslandandthePavelBrassillegallypassedfromtheStatepropertytopropertyofTeleormanCountyandintheadministra6onofTeleormanCountyCouncil,justafewdaysa_er,theyalsobeingillegallyleasedtoaprivatecompany. temporalexpression

date

13

Nameen66esOnSeptember22,DNAannouncedthatDeputyPrimeMinisterSevilShhaidehissuspectedofcommi[ngserviceabusewhenhewasastatesecretaryatMDRAP,inadossierthat,in2013,throughtheconcertedac6onofsomepublicofficials,partsoftheBelinaIslandandthePavelBrassillegallypassedfromtheStatepropertytopropertyofTeleormanCountyandintheadministra6onofTeleormanCountyCouncil,justafewdaysa_er,theyalsobeingillegallyleasedtoaprivatecompany.

Ins6tu6on

14

Nameen66esOnSeptember22,DNAannouncedthatDeputyPrimeMinisterSevilShhaidehissuspectedofcommi[ngserviceabusewhenhewasastatesecretaryatMDRAP,inadossierthat,in2013,throughtheconcertedac6onofsomepublicofficials,partsoftheBelinaIslandandthePavelBrassillegallypassedfromtheStatepropertytopropertyofTeleormanCountyandintheadministra6onofTeleormanCountyCouncil,justafewdaysa_er,theyalsobeingillegallyleasedtoaprivatecompany.

geoloca6ons

15

Lemmaandthepartofspeech

Solicitat–solicita–vbsă–să–conjcomenteze–comenta–vbun–un–art.nehot.editorial–editorial–sbrecent–recent–adj...

16

•  English

Morphologicalannota6on

0 1 He he subj:>2 @SUBJPRON2 did do v-ch:>4 @+FAUXVV3 not not neg:>2 @ADVLNEG-PART4 knowknowmain:>0 @-FMAINVV5 her she subj:>6 @OBJPRON6 namenameobj:>4@-FMAINVV

•  Romanian

<TOKID="TOK478"root="Nu"pv="Par6cle"Type="nega6on">Nu</TOK><TOKID="TOK479"root="ş6"pv="Verb"Type="main"Mood="indic."

Tense="imperfect"Person="third"Number="singular">ş6a</TOK><TOKID="TOK480"root="cum"pv="Adverb"type="int_rel">cum</TOK><TOKID="TOK481"root="el"pv="Pronoun"Type="pers"Person="third"

Gender="feminine"Number="singular"Case="accusa6ve">o</TOK><TOKID="TOK482"root="chema"pv="Verb"Type="main"Mood="indic."

Tense="present"Person="third">cheamă</TOK>

17

Nominalphrases

Solicitatsăcomenteze[uneditorialrecentallui[DinuPatriciu]],în[care][acesta]precizacănucredeîn[social-liberalism]şisăapreciezedacă,asfel,adat[oloviturăde[imagine]][USL],[Antonescu]aspuscănuş6edacă[Patriciu]s-areferitla[USL].

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<NPID="NP903"HEADID="W3190"VERBPOS="W3191"><WID="W3190"POS="PRON"NUM="SG"GENDER="M"ROLE="SUBJ" LEMMA="he"LINK="W3191"LINKTYPE="subj">He</W></NP>

<WID="W3191"POS="V"ROLE="+FAUXV"LEMMA="do"LINK="W3193"LINKTYPE="v-ch">did</W>

<WID="W3192"POS="NEG-PART"ROLE="ADVL"LEMMA="not"LINK="W3191"LINKTYPE="neg">not</W>

<WID="W3193"POS="V"ROLE="-FMAINV"LEMMA="know"LINK="W3189"LINKTYPE="main">know</W>

<NPID="NP1188"HEADID="W3195"><NPID="NP904"HEADID="W3194"VERBPOS="W3189"> <WID="W3194"POS="PRON"NUM="SG"GENDER="F” ROLE="OBJ"LEMMA="she"LINK="W3195“LINKTYPE="subj">her</W></NP><WID="W3195"POS="V"ROLE="-FMAINV"LEMMA="name”LINK="W3193"LINKTYPE="obj">name</W></NP>

Annota6onofnominalphrases

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WriGenlanguagetechnologies

•  Languageanalysisandunderstanding– syntac6cprocessing

•  gramma6calformalisms•  parsingèsyntac6cstructureofthesentence

20

Syntac6cambigui6es

Mariapriveştecalulcuochelari.(Mariawatchesthehorsewithspectacles.)

VP

priveşte

S

Maria

NP

calul

PP

NP

cu ochelari

NP

VP

priveşte

S

Maria

NP

calul

NP

cu ochelari

NP

PP

21

ElementaryNLPtools

•  Tokenizer:getthewordboundaries–  Input:rawtext– Output:<tok id=“...”>word</tok>– How:byregularexpressions

22

ElementaryNLPtools

•  POS-Tagger:Part-of-Speechtagging(morfo-syntac6cdisambigua6on)–  Input:<tok id=“...”>word</tok>– Output:<tok id=“...” POS=“...”>word</tok>– How:byexploi6ngthefrequenciesofoccuranceofadjacentPOSs=>op6misegloballythesequenceoftags

Thesawmadenoise.

DET VN

NV

N

23

ElementaryNLPtools

•  Lemma_ser:getthebaseformofwords–  Input:<tok id=“...” POS=“...”>word</tok>– Output:<tok id=“...” POS=“...” lemma=“...”>word</tok>

– How:byusingadic6onaryandexploi6ngfrequenciesofoccuranceofadjacentlemmas

Thesawmadenoise.

the sawsee

mademake

noise

24

ElementaryNLPtools

•  NP-Chunker:detectNounPhrases–  Input:sequenceof<tok>elements– Output:<npid=“...”>...</np>– How:applyregularexpressions

25

ElementaryNLPtools

•  NER:recogniseNameEn66esandclassifythem–  Input:rawtext– Output:<neid=“...”type=“...”>...</ne>– How:basedonregularexpressions,largelistsofen6tynamesspecialisedperlanguage(gazeteers)

26

WriGenlanguagetechnologies

•  Languageanalysisandunderstanding– Seman6canddiscourseprocessing

•  seman6cdisambigua6onèwordsenses•  seman6croleslabelling•  rhetoricalstructureofdiscourseanddialogue•  anaphoraresolu6on•  textsummarisa6on

27

Coreferential chains

Winston was just taking his place in one of the middle rows when two people whom he knew by sight, but had never spoken to, came unexpectedly into the room. One of them was a girl whom he often passed in the corridors. He did not know her name, but he knew that she worked in the Fiction Department.

28

Coreferential chains

Winston was just taking his place in one of the middle rows when two people whom he knew by sight, but had never spoken to, came unexpectedly into the room. One of them was a girl whom he often passed in the corridors. He did not know her name, but he knew that she worked in the Fiction Department.

29

Coreferential chains

Winston was just taking his place in one of the middle rows when two people whom he knew by sight, but had never spoken to, came unexpectedly into the room. One of them was a girl whom he often passed in the corridors. He did not know her name, but he knew that she worked in the Fiction Department

30

Coreferential chains

Winston was just taking his place in one of the middle rows when two people whom he knew by sight, but had never spoken to, came unexpectedly into the room. One of them was a girl whom he often passed in the corridors. He did not know her name, but he knew that she worked in the Fiction Department.

31

Wordsensesaredeterminedbytheircontexts

•  Ionseprinseînhorăcuofatăcucosiţelungi.•  Cândfatăiapata?

•  Mămaidauodatăpepâr(aroşie.•  I-amdatunapestemână.•  Mariaadatcarteaînapoi.

•  M-amscos…•  Mi-amscosmăseauademinte.

32

Pre-processing

TEXT

TOK

Tokeniser

TOK(POS,LEM)

POS-tagger+Lemma6ser

SENT

SENT-SPLITTER

33

NP-chunking,NER

TOK+NP

NP-chunker

TOK+NP+NE(PER)

TOK(POS,LEM)

TOK+NP+NE(TIME)

NER_6me

TOK+NP+NE(ORG)

NER_orgNER_person

TOK+NP+NE

merge

34

Coreference,syntac6cparsing

TOK+NP+NE+COREF

TOK+NP+NE

TOK+SENT+SYNT

TOK+NP+NE+COREF+SENT+SYNT

merge

RARE

TOK(POS,LEM) SENT

TOK+SENT

FDG-parser

merge

35

Events

EVENT-finder

TOK+NP+NE+COREF+SENT+SYNT

TOK+NP+NE+COREF+SENT+SYNT+EVENT

TEXT

EVENT

simplifica6on

manualannota6on

36

KinshipandSpace

KINSHIP-finder

TOK+NP+NE+COREF+SENT+SYNT

TOK+NP+NE+COREF+SENT+SYNT+KINSHIP

TEXT

KINSHIP SPACE

SPACE-finder

simplifica6onsimplifica6on

manualannota6on

manualannota6on

TOK+NP+NE+COREF+SENT+SYNT+SPACE

37

Kinshiprela6ons:exemple

-Lascăcu(nemărăfuiescdupă,îiscăpăprintredințiomuluieiDonca,nevastacălugăruluizbanghiuZuicu,care-IaduseselaelacasăpeIonșipepreședinte.

Aposi6on:Per-X,Rel(atrib)Per-Ygen,=>marriage(X:person[sex:?],Y:person[sex:?])

marriage(Donca:person[sex:f],Zuicu:person[sex:m])

38

Kinshiprela6ons:exemple-Forfortyyears,EllaRubinstein1’slifewasasastandingwater…Her1husband,David,wasasuccessfulden(st…

Aposi6on:Per-Xpron,genRel,Per-Y,=>marriage(antecedent(X):person[sex:?],Y:person[sex:?])

marriage(EllaRubistein:person[sex:f],David:person[sex:m])

39

Spa6alrela6onships:exemple

AtfiveverstsofAremzianskyurts,inthemiddleoftheriverIrtâş,liestheislandofKuntai.Filatov'svillageisonthele_bank,twoverstsfromtheisland.

40

Anexampleofcallibra6onofaModule

SupposewewanttobuildaToolperformingaspecifictask.Thenwehavetobuild3modules:§ TheTrainingModule(TM)§ TheToolitself(X)§ TheEvalua_onModule(EM)

41

•  ThismoduleextractsamodelwhichwillbeusedbytheTool.

TheTrainingModule

TrainingModuleTrainingCorpus

model

preferencesTraining.pref

42

•  Thismoduleappliesanalgorithmontheinputandtransformsitaccordingtothelearnedmodel.

TheModuleX

ThemoduleX output.xml

input.xml

preferencesX.pref

model

43

•  ThismoduleevaluatestheTestfileagainsttheGoldfile.

TheEvalua_onModule

Evalua6onModule evalLog

preferencesEvalua6on.pref

output.xml

gold.xml

Test

44

Evalua6onmeasures

•  Precision=#itemiîncomunînTest&Gold/#itemiînTest•  Recall=#itemiîncomunînTest&Gold/#itemiînGold•  F-measure=2*P*R/(P+R)

45

GeneralArhitecture

TMTrainingcorpus

model

preferencesTraining.pref

X

EM

preferencesEvalua6on.pref

input.xml

output.xml

gold.xml

evalLog

preferencesX.pref

46

Calibra6onsystem

TM

X

EM

configura6on.cfg

TrainingCorpus

input.xml

gold.xml

preferencesTraining.pref

preferencesSegmenter.pref

C

Op6malvalues

preferencesEvalua6on.pref

47

AnyelementaryNLPtoolpar6cipa6nginaprocessingchain

ModuleXstandardinput

standardoutput

outputinput

resourses

standardresourses

parameters

txt

48

Eventshappenin6me

Cândaintratîncamerăe1,Ionaaprinsluminae2.Dupăcinciminuteaieşite3.Laieşireas(nsluminae4.

49

Eventshappenin6me

Cândaintratîncamerăe1,Ionaaprinsluminae2.Dupăcinciminuteaieşite3.Laieşireas(nsluminae4.

Twotypesoftemporalexpressions:•  instants...

e1:t1/e2:t1/e3:t2=t1+5min/e4:t2

time

e1

t1

e3

t2

e2 e4 50

Eventshappenin6me

Cândaintratîncamerăe1,Ionaaprinsluminae2.Dupăcinciminuteaieşite3.Laieşireas(nsluminae4.

Twotypesoftemporalexpressions:•  ...andintervals:

time

e1

t1

e3

t2

e2 e4

5 minutes

51

Eventscanbe...

•  Instantaneous:Ionaieşitdincameră.Marias-aîntâlnitcuprofuldemate.

•  Take6me:Ionaci(ttoatăseara.Afarăplouă.

time t

e

time t1

e

t2

52

Signalsfortemporalrela6ons

Cândaintratîncamerăe1,Ionaaprinsluminae2.Dupăcinciminuteaieşite3.Laieşireas(nsluminae4.

cândei,ejèt(ei)=t(ej)ei.După<interval>ejèt(ej)=t(ei)+<interval>La<reference(ei)>ejèt(ei)=t(ej)

timp

e1

t1

e3

t2

e2 e4

5 minute

53

Typeofreasoningwhere6memaGers

2. [Falimentul firmei] a avut loc la un an după [înfiinţarea ei].

un an

1. Samurai S.R.L. a luat fiinţă la 23 ianuarie 1984.

Când a falimentat Samurai S.R.L.?

54

Processingstatements

a luat fiinţă

Samurai S.R.L.(id=obj1) subj

la

23 ianuarie 1984

compl

PP ev1

23ianuarie1984

ISA aluafiinţă

SamuraiS.R.L. REC

TIME

evenimential representation

<object ID=“obj1” ISA=“companie” NAME=“Samurai S.R.L.”/>

<event ID=“ev1” ISA=“a_lua_fiinţă” REC=“obj1” TIME=“23.01.1984”/>

55

simplificări

a avut loc

falimentarea subj

la

an

compl

atrib. genit? firmei

un după

înfiinţarea

ei

PP

atrib. genit?

det

a avut loc

falimentarea subj

la

an

compl

atrib. genit? obj1

un după

înfiinţarea

obj1

PP

atrib. genit?

det

rezoluţia anaforelor

a falimenta subj

la

an

compl obj1

un după

ev1

PP

det

dacă are_loc falimentarea lui X atunci X falimentează

referinţă anaforică la un eveniment deja menţionat

56

Processingstatements

evenimential representations

a falimenta subj

la

an

compl obj1

un după

ev1

PP

det temporal expression

anchored in another event

<event ID=“ev2” ISA=“a_falimenta” REC=“obj1” TIME=“timex1”/>

<timex ID=“timex1” TYPE=“after” REF=“ev1” DUR=“1” UNIT=“year”/>

57

Compu6ng6me<objectID=“obj1”ISA=“companie”NAME=“SamuraiS.R.L.”/><eventID=“ev1”ISA=“a_lua_fiinţă”REC=“obj1”TIME=“23.01.1984”/><eventID=“ev2”ISA=“a_falimenta”REC=“obj1”TIME=“6mex1”/><6mexID=“6mex1”TYPE=“a_er”REF=“ev1”DUR=“1”UNIT=“year”/><eventID=“ev2”ISA=“a_falimenta”REC=“obj1”TIME=“23.01.1985”/>

58

TheQuoVadiscorpus

59

Acorpusofen66esandseman6crela6ons

•  En66es–types:–  persons–  gods–  groupsofpersonsandgods–  bodyparts

•  Seman6c rela6onsmen6onedbetween theseen66es

60

En66es–examples

•  Characters (Marcus Vinicius, Ligia), groups (theChris(ans,thesoldiers);

•  At the text level: nominal groups (the youngpatrician,sonoftheconsul);

•  Imbricatedreferen6alexpressions:[daughter[ofAulus]2]1

61

Typesofrela6ons

•  Anaphoric•  Seman6c

– kinship– affec6ve– social

62

Anaphoricrela6ons•  coref•  coref-interpret•  member-of,has-as-member(inverse)•  isa,class-of(inverse)•  part-of,has-as-part(inverse)•  subgroup-of,has-as-subgroup(inverse)•  has-name,name-of(inverse)1:[Acteea]...2:[theyounglibert]...=>[2]coref[1]1:[2:[his]righthand]=>[1]part-of[2]

63

Kinshiprela6ons•  parent-of•  child-of(inverseofparent-of)•  grandparent-ofandgrandchild-of(inverse)•  sibling(symmetrical)•  ant-uncle-of,nephew-of(inverserela6on)•  cousin-of(symmetrical)•  spouse-of(symmetrical)•  unknown

1:[thesecondhusbandof2:[Popeia]]=>[1]spouse-of[2]1:[sisterof2:[Petronius]]=>[1]sibling-of[2]

64

Socialrela6ons

•  superior-of•  inferior-of•  incoopera(on-with•  colleague-of•  incompe((on-with•  opposite-toLibera(ng1:[her],2:[Nero]…=>[2]superior-of[1]1:[Theyoungman]foughtunderthecommandof2:[Corbulon]=>[1]inferior-of[2]

65

Affec6verela6ons•  love•  loved-by•  hate•  hatedby•  upset•  friendship•  worship•  angerPe1:[Vinicus]îlcuprinseomânienăprasnicăîmpotriva

2:[împăratului]șiîmpotriva3:[Acteii]=>[1]anger[2],[1]anger[3]

66

<ENTITYID="E8"TYPE="PERSON"><Wid="28"LEMMA="Marcus">Marcus</W><Wid="29"LEMMA="Vinicius">Vinicius</W></ENTITY><Wid="30"LEMMA="fi">era</W><KINSHIPID="KIN57"FROM="E12"TO="E11"TRIGGER="31"

TYPE="child-of"><ENTITYID="E12"TYPE="PERSON"><Wid="31"LEMMA="fiu">fiul</W><KINSHIPID="KIN53"FROM="E11"TO="E10"TRIGGER="32"

TYPE="sibling-of"><ENTITYID="E11"TYPE="PERSON"><Wid="32"LEMMA="soră">surorii</W><ENTITYID="E10"TYPE="PERSON"><Wid="33"LEMMA="său">sale</W></ENTITY><Wid="34"LEMMA="mai">mai</W><Wid="35"LEMMA="mare">mari</W></ENTITY></KINSHIP></ENTITY></KINSHIP><Wid="36"LEMMA=",">,</W><KINSHIPID="KIN59"FROM="E13"TO="E15"TRIGGER="44"

TYPE="spouse-of"><ENTITYID="E13"TYPE="PERSON"><Wid="37"LEMMA="care">care</W></ENTITY><Wid="38"LEMMA=",">,</W><Wid="39"LEMMA="cu">cu</W><Wid="40"LEMMA="an">ani</W><Wid="41"LEMMA="în_urmă">înurmă</W><Wid="42"LEMMA=",">,</W><Wid="43"LEMMA="sine">se</W>

<Wid="44"LEMMA="căsători">căsătorise</W><Wid="45"LEMMA="cu">cu</W><KINSHIPID="KIN61"FROM="E15"TO="E14"TRIGGER="46"TYPE="parent-of"><ENTITYID="E15"TYPE="PERSON"><Wid="46"LEMMA="tată">tatăl</W><ENTITYID="E14"TYPE="PERSON"><Wid="47"LEMMA="acesta">acestuia</W></ENTITY></ENTITY></KINSHIP></KINSHIP><SOCIALID="SOC9"FROM="E17"TO="E16"TRIGGER="49"TYPE="inferior-of"><ENTITYID="E17"TYPE="PERSON"><Wid="49"LEMMA="consul">consul</W><Wid="50"LEMMA="pe">pe</W><Wid="51"LEMMA="vreme">vremea</W><Wid="52"LEMMA="el">lui</W><ENTITYID="E16"TYPE="PERSON"><Wid="53"LEMMA="Tiberiu">Tiberiu</W></ENTITY></ENTITY></SOCIAL><Wid="54"LEMMA=".">.</W><REFERENTIALID="REF37"FROM="E12"TO="E8"TYPE="coref"/REFERENTIAL><REFERENTIALID="REF38"FROM="E13"TO="E11"TYPE="coref"/REFERENTIAL><REFERENTIALID="REF39"FROM="E14"TO="E8"TYPE="coref"/REFERENTIAL><REFERENTIALID="REF40"FROM="E17"TO="E15"TYPE="class-of"/REFERENTIAL>

Anno

ta6o

n

67

Sta6s6csinvolvingthecorpus

•  7.281sentences•  146.822wordsandpunctua6onsigns•  24.636en66esmen6ons•  22.301referen6alrela6ons•  755AKSrela6ons(Affec6ve+Kinship+Social)•  752triggers

68

Example:rela6onsloveandworship

69

Affec6verela6onsfear-ofandhate

70

Rela6onsbetweenViniciusandothercharacters

71

Distribu6onofseman6crela6onsinvolvingVinicius

72

Linguis(csLinkedOpenData(LLOD)

-  Developmentoftechniquesthatwillallowtodeciphertheseman_ccontentoftexts-  abstracts(general,focusedoncertaincharacters),-  narra6velines(e.g.theevolu6onoffeelingsbetweenViniciusandLigia)

-  sta6cconnec6onsbetweenen66es(e.g.genealogictrees),

-  sta6s6csoveren66es(e.g.domina6ngsen6mentsamongChris6ansascomparedtothoseamongRomans)

73

Linguis(csLinkedOpenData(LLOD)

-  Genera6onofontologiesfromtrea6es-  applica6onsableto“read”textsreferingtoadomainandtoformalisetheconceptsandtheirrela6ons

-  Intelligentdocumentsretrieval-  personalisedagentsintheresearchac6vity

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