1 from wordnet, to eurowordnet, to the global wordnet grid: anchoring languages to universal meaning...
TRANSCRIPT
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From WordNet, to EuroWordNet,
to the Global Wordnet Grid: anchoring languages to universal meaning
Piek Vossen
VU University Amsterdam
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What kind of resource is wordnet?
• Mostly used database in language technology
• Enormous impact in language technology development
• Large
• Free and downloadable
• English
WordNet http://wordnet.princeton.edu/http://wordnet.princeton.edu/• Developed by George Miller and his team at
Princeton University, as the implementation of a mental model of the lexicon
• Organized around the notion of a synset: a set of synonyms in a language that represent a single concept
• Semantic relations between concepts• Covers over 117,000 concepts and over
150,000 English words
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Relational model of meaning
man woman
boy girl
cat
kitten
dog
puppy
animal
man
woman
boy
meisje
cat
kitten
dogpuppy
animal
Wordnet: a network of semantically related words
{conveyance;transport}
{vehicle}
{motor vehicle; automotive vehicle}
{car; auto; automobile; machine; motorcar}{bumper}
{car door}
{car window}
{car mirror} {armrest}
{doorlock}
{hinge; flexible joint}
{cruiser; squad car; patrol car; police car; prowl car}
{cab; taxi; hack; taxicab}
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Wordnet Semantic RelationsWordnet Semantic Relations
WN 1.5 starting point
The ‘synset’ as a weak notion of synonymy:“two expressions are synonymous in a linguistic context C if the substitution of one for the other in C does not alter the truth value.” (Miller et al. 1993)
Relations between synsets:Relation POS-combination ExampleANTONYMY adjective-to-adjective good/bad
verb-to-verb open/ closeHYPONYMY noun-to-noun car/ vehicle
verb-to-verb walk/ moveMERONYMY noun-to-noun head/ noseENTAILMENT verb-to-verb buy/ payCAUSE verb-to-verb kill/ die
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Wordnet Data Model
bank
fiddleviolin
violistfiddler
string
rec: 12345- financial instituterec: 54321
- side of a riverrec: 9876
- small string instrumentrec: 65438
- musician playing violinrec:42654
- musician
rec:25876
- string instrument
rec:35576
- string of instrumentrec:29551
- underwear
type-of
type-of
part-of
Vocabulary of a languageConceptsRelations
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2
2
1
1
2
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Some observations on Wordnet
• synsets are more compact representations for concepts than word meanings in traditional lexicons
• synonyms and hypernyms are substitutional variants:– begin – commence– I once had a canary. The bird got sick. The poor animal died.
• hyponymy and meronymy chains are important transitive relations for predicting properties and explaining textual properties:object -> artifact -> vehicle -> 4-wheeled vehicle -> car
• strict separation of part of speech although concepts are closely related (bed – sleep) and are similar (dead – death)
• lexicalization patterns reveal important mental structures
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Lexicalization patterns
25 unique beginnersgarbage
tree
organism
animal
bird
canarychurch
building
artifact
object
plant
flower
rose
wastethreat
entity
common canary
abbey
crocodiledogbasic level concepts
• balance of two principles: • predict most features• apply to most subclasses
• where most concepts are created • amalgamate most parts• most abstract level to draw a pictures
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Wordnet top level
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Meronymy & picturesbeak
tail
leg
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Meronymy & pictures
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Co-reference constraint in wordnet:Cats cannot be a kind of cats
• S: (n) cat, true cat (feline mammal usually having thick soft fur and no ability to roar: domestic cats; wildcats)
• S: (n) guy, cat, hombre, bozo (an informal term for a youth or man) "a nice guy"; "the guy's only doing it for some doll"
• S: (n) cat (a spiteful woman gossip) "what a cat she is!" • S: (n) kat, khat, qat, quat, cat, Arabian tea, African tea (the leaves of the shrub Catha
edulis which are chewed like tobacco or used to make tea; has the effect of a euphoric stimulant) "in Yemen kat is used daily by 85% of adults"
• S: (n) cat-o'-nine-tails, cat (a whip with nine knotted cords) "British sailors feared the cat"
• S: (n) Caterpillar, cat (a large tracked vehicle that is propelled by two endless metal belts; frequently used for moving earth in construction and farm work)
• S: (n) big cat, cat (any of several large cats typically able to roar and living in the wild) • S: (n) computerized tomography, computed tomography, CT, computerized axial
tomography, computed axial tomography, CAT (a method of examining body organs by scanning them with X rays and using a computer to construct a series of cross-sectional scans along a single axis)
• S: (n) domestic cat, house cat, Felis domesticus, Felis catus (any domesticated member of the genus Felis)
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Wordnet 3.0 statistics
POS Unique Synsets Total
Strings Word-Sense
Pairs
Noun 117,798 82,115 146,312
Verb 11,529 13,767 25,047
Adjective 21,479 18,156 30,002
Adverb 4,481 3,621 5,580
Totals 155,287 117,659 206,941
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Wordnet 3.0 statistics
POS Monosemous Polysemous Polysemous
Words and
Senses Words Senses
Noun 101,863 15,935 44,449
Verb 6,277 5,252 18,770
Adjective 16,503 4,976 14,399
Adverb 3,748 733 1,832
Totals 128,391 26,896 79,450
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Wordnet 3.0 statistics
POS Average Polysemy Average Polysemy
Including Monosemous
Words Excluding Monosemous
Words
Noun 1.24 2.79
Verb 2.17 3.57
Adjective 1.4 2.71
Adverb 1.25 2.5
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http://www.visuwords.com
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Usage of Wordnet
• Improve recall of textual based analysis:– Query -> Index
• Synonyms: commence – begin• Hypernyms: taxi -> car• Hyponyms: car -> taxi• Meronyms: trunk -> elephant• Lexical entailments: gun -> shoot
• Inferencing:– what things can burn?
• Expression in language generation and translation:– alternative words and paraphrases
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Improve recall
• Information retrieval: – small databases without redundancy, e.g. image
captions, video text
• Text classification:– small training sets
• Question & Answer systems– query analysis: who, whom, where, what, when
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Improve recall
• Anaphora resolution:– The girl fell off the table. She....– The glass fell of the table. It...
• Coreference resolution:– When he moved the furniture, the antique table got
damaged.
• Information extraction (unstructed text to structured databases):– generic forms or patterns "vehicle" - > text with
specific cases "car"
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Improve recall
• Summarizers:– Sentence selection based on word counts ->
concept counts– Avoid repetition in summary -> language
generation
• Limited inferencing: detect locations, organisations, etc.
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Many others
• Data sparseness for machine learning: hapaxes can be replaced by semantic classes
• Use redundancy for more robustness: spelling correction and speech recognition can built semantic expectations using Wordnet and make better choices
• Sentiment and opinion mining• Natural language learning
Recall & Precision
query:
“cell”
“cell
phone”
“mobile
phones”
“nerve cell”
“police cell”
recall = doorsnede / relevant
precision = doorsnede / gevonden
found intersection relevant
Recall < 20% for basic search engines!
(Blair & Maron 1985)
“jail”
“neuron”
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EuroWordNet
• The development of a multilingual database with wordnets for several European languages
• Funded by the European Commission, DG XIII, Luxembourg as projects LE2-4003 and LE4-8328
• March 1996 - September 1999
• 2.5 Million EURO.
• http://www.hum.uva.nl/~ewn
• http://www.illc.uva.nl/EuroWordNet/finalresults-ewn.html
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EuroWordNetEuroWordNet
• Languages covered: – EuroWordNet-1 (LE2-4003): English, Dutch, Spanish, Italian– EuroWordNet-2 (LE4-8328): German, French, Czech, Estonian.
• Size of vocabulary:– EuroWordNet-1: 30,000 concepts - 50,000 word meanings.– EuroWordNet-2: 15,000 concepts- 25,000 word meaning.
• Type of vocabulary: – the most frequent words of the languages– all concepts needed to relate more specific concepts
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EuroWordNet Model
I = Language Independent linkII = Link from Language Specific to Inter lingual IndexIII = Language Dependent Link
III
Lexical Items Table
cavalcare
andaremuoversi
III
guidare
ILI-record{drive}
Inter-Lingual-Index
Ontology
2OrderEntity
Location Dynamic
Domains
Traffic
Air Road` III
Lexical Items Table
bewegengaan
rijden berijden
III
Lexical Items Table
driveride
movego
III
III
Lexical Items Table
cabalgar jinetear
III
conducir
movertransitar
IIIII
IIII
II
I I
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Differences in relations between Differences in relations between EuroWordNet and WordNetEuroWordNet and WordNet
• Added Features to relations
• Cross-Part-Of-Speech relations
• New relations to differentiate shallow hierarchies
• New interpretations of relations
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EWN Relationship LabelsEWN Relationship Labels{airplane} HAS_MERO_PART: conj1 {door}
HAS_MERO_PART: conj2 disj1 {jet engine}HAS_MERO_PART: conj2 disj2 {propeller}
{door} HAS_HOLO_PART: disj1 {car}HAS_HOLO_PART: disj2 {room}
HAS_HOLO_PART: disj3 {entrance}
{dog} HAS_HYPERONYM: conj1 {mammal} HAS_HYPERONYM: conj2 {pet}
{albino} HAS_HYPERONYM: disj1 {plant} HAS_HYPERONYM: disj2 {animal}
Default Interpretation: non-exclusive disjunction
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Factive/Non-factive CAUSES (Lyons 1977)
factive (default interpretation):
“to kill causes to die”: {kill} CAUSES{die}
non-factive: E1 probably or likely causes event E2 or E1 is intended to cause some event E2:
“to search may cause to find”.{search} CAUSES {find} non-factive
EWN Relationship LabelsEWN Relationship Labels
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Cross-Part-Of-Speech relationsCross-Part-Of-Speech relations
WordNet1.5: nouns and verbs are not interrelated by basic semantic relations such as hyponymy and synonymy:
adornment 2 change of state-- (the act of changing something)adorn 1 change, alter-- (cause to change; make different)
EuroWordNet: words of different parts of speech can be inter-linked with explicit xpos-synonymy, xpos-antonymy and xpos-hyponymy relations:
{adorn V} XPOS_NEAR_SYNONYM {adornment N}
{size N} XPOS_NEAR_HYPONYM {tall A}{short A}
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Role relationsRole relations
In the case of many verbs and nouns the most salient relation is not the hyperonym but the relation between the event and the involved participants. These relations are expressed as follows:
{knife} ROLE_INSTRUMENT {to cut}{to cut} INVOLVED_INSTRUMENT {knife} reversed{school} ROLE_LOCATION {to teach}{to teach} INVOLVED_LOCATION {school} reversed
These relations are typically used when other relations, mainly hyponymy, do not clarify the position of the concept network, but the word is still closely related to another word.
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Co_Role relationsCo_Role relations
guitar player HAS_HYPERONYM playerCO_AGENT_INSTRUMENT guitar
player HAS_HYPERONYM personROLE_AGENT to play musicCO_AGENT_INSTRUMENT musical instrument
to play music HAS_HYPERONYM to makeROLE_INSTRUMENT musical instrument
guitar HAS_HYPERONYM musical instrumentCO_INSTRUMENT_AGENT guitar player
ice saw HAS_HYPERONYM sawCO_INSTRUMENT_PATIENT ice
saw HAS_HYPERONYM sawROLE_INSTRUMENT to saw
ice CO_PATIENT_INSTRUMENT ice saw REVERSED
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Co_Role relationsCo_Role relations
Examples of the other relations are:
criminal CO_AGENT_PATIENT victimnovel writer/ poet CO_AGENT_RESULT novel/ poemdough CO_PATIENT_RESULT pastry/ breadphotograpic camera CO_INSTRUMENT_RESULT photo
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Overview of the Language Internal Overview of the Language Internal relations in EuroWordnetrelations in EuroWordnet
Same Part of Speech relations:NEAR_SYNONYMY apparatus - machineHYPERONYMY/HYPONYMY car - vehicleANTONYMY open - closeHOLONYMY/MERONYMY head - nose
Cross-Part-of-Speech relations:XPOS_NEAR_SYNONYMY dead - death; to adorn - adornmentXPOS_HYPERONYMY/HYPONYMY to love - emotionXPOS_ANTONYMY to live - deadCAUSE die - deathSUBEVENT buy - pay; sleep - snoreROLE/INVOLVED write - pencil; hammer - hammerSTATE the poor - poorMANNER to slurp - noisily BELONG_TO_CLASS Rome - city
chronical patient ; mental patient
patient
HYPONYM
ρ-PROCEDURE ρ-LOCATION
STATE
ρ-CAUSE
cureρ-PATIENT
treat
docter
disease; disorder
physiotherapymedicineetc.
hospital, etc.
stomach disease, kidney disorder,
ρ-PATIENT ρ-AGENT
child docter
child
co-ρ-AGENT-PATIENT
Horizontal & vertical semantic relations
HYPONYM
HYPONYM
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• Inter-Lingual-Index: unstructured fund of concepts to
provide an efficient mapping across the languages;
• Index-records are mainly based on WordNet synsets and
consist of synonyms, glosses and source references;
• Various types of complex equivalence relations are
distinguished;
• Equivalence relations from synsets to index records: not on a
word-to-word basis;
• Indirect matching of synsets linked to the same index items;
The Multilingual DesignThe Multilingual Design
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Equivalent Near SynonymEquivalent Near Synonym1. Multiple Targets (1:many)
Dutch wordnet: schoonmaken (to clean) matches with 4 senses of clean in WordNet1.5:• make clean by removing dirt, filth, or unwanted substances from• remove unwanted substances from, such as feathers or pits, as of chickens or fruit• remove in making clean; "Clean the spots off the rug"• remove unwanted substances from - (as in chemistry)
2. Multiple Sources (many:1)Dutch wordnet: versiersel near_synonym versiering ILI-Record: decoration.
3. Multiple Targets and Sources (many:many)Dutch wordnet: toestel near_synonym apparaat
ILI-records: machine; device; apparatus; tool
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Equivalent HyperonymyTypically used for gaps in English WordNet:
• genuine, cultural gaps for things not known in English culture:
– Dutch: klunen, to walk on skates over land from one frozen water to the other
• pragmatic, in the sense that the concept is known but is not expressed by a single lexicalized form in English:
– Dutch: kunststof = artifact substance <=> artifact object
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Equivalent Hyponymy
has_eq_hyponym
Used when wordnet1.5 only provides more narrow terms. In this case there can only be a pragmatic difference, not a genuine cultural gap, e.g.: Spanish dedo = either finger or toe.
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{ toe : part of foot }
{ finger : part of hand }
{ dedo , dito : finger or toe } { head : part of body } { hoofd : human head } { kop : animal head }
toe finger head
dito
dedo
hoofd kop
EN-Net
NL-Net
IT-Net
ES-Net
= normal equivalence
= eq _has_hyponym
= eq _has_hyperonym
Complex mappings across languages
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Typical gaps in the (English) ILI• Dutch:doodschoppen (to kick to death):
eq_hyperonym {kill}V and to {kick}V aardig (Adjective, to like):
eq_near_synonym {like}Vcassière (female cashier)
eq_hyperonym {cashier}, {woman}kunstproduct (artifact substance)
eq_hyperonym {artifact} and to {product}
• Spanish:alevín (young fish):
eq_hyperonym {fish} and eq_be_in_state {young}cajera (female cashier)
eq_hyperonym {cashier}, {woman}
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Wordnets as semantic structures
• Wordnets are unique language-specific structures:– different lexicalizations– differences in synonymy and homonymy– different relations between synsets– same organizational principles: synset structure and
same set of semantic relations.
• Language independent knowledge is assigned to the ILI and can thus be shared for all language linked to the ILI: both an ontology and domain hierarchy
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Autonomous & Language-Specific
voorwerp{object}
lepel{spoon}
werktuig{tool}
tas{bag}
bak{box}
blok{block}
lichaam{body}
Wordnet1.5 Dutch Wordnet
bagspoonbox
object
natural object (an object occurring naturally)
artifact, artefact (a man-made object)
instrumentality block body
containerdeviceimplement
tool instrument
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Artificial ontology: • better control or performance, or a more compact and coherent structure. • introduce artificial levels for concepts which are not lexicalized in a language (e.g. instrumentality, hand tool), • neglect levels which are lexicalized but not relevant for the purpose of the ontology (e.g. tableware, silverware, merchandise).
What properties can we infer for spoons?spoon -> container; artifact; hand tool; object; made of metal or plastic; for eating, pouring or cooking
Linguistic versus Artificial Ontologies
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Linguistic ontology: • Exactly reflects the relations between all the lexicalized words and
expressions in a language. • Captures valuable information about the lexical capacity of
languages: what is the available fund of words and expressions in a language.
What words can be used to name spoons?spoon -> object, tableware, silverware, merchandise, cutlery,
Linguistic versus Artificial Ontologies
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Wordnets versus ontologies
• Wordnets:• autonomous language-specific lexicalization
patterns in a relational network. • Usage: to predict substitution in text for
information retrieval,• text generation, machine translation, word-
sense-disambiguation.• Ontologies:
• data structure with formally defined concepts.• Usage: making semantic inferences.
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Sharing world knowledge
• All wordnets in the world can be linked to the same ontology
• All wordnets in the world can be linked to the same thesaurus
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Wordnet: Domain information
type-of
type-ofpart-of
Relations
rec: 12345- financial institute
rec: 54321
- river side
rec: 9876
- small string instrument
rec: 65438
- musician playing a violin
rec:42654
- musician
rec:25876
- string instrument
rec:35576
- string of an instrument
rec:29551
- underwear
ConceptsVocabularies of languages
bank
violin
violist
string
1
2
1
2
1
2
Domains
Music
Culture FinanceClothing Sport
Ball
sports
Winter
sports
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How to harmonize wordnets?
• Wordnets are unique language-specific lexicalizations patterns
• Define universal sets of concepts that play a major role in many different wordnets: so-called Base Concepts
• Define base concepts in each language wordnet– High level in the hierarchy– Many hyponyms
• Provide the closest equivalent in English wordnet• Determine the intersection of English
equivalences
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Lexicalization patterns
25 unique beginnersgarbage
tree
organism
animal
bird
canarychurch
building
artifact
object
plant
flower
rose
threat
entity
common canary
abbey
crocodiledogbasic level concepts
1024 base concepts
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Base Concept Intersection
Nouns Verbs
Intersection EN, NL, IT, ES 24 6
Intersection FR, DE, EE, CZ 70 30
Intersection All 13 2
{cause 6; get#9; have#7; induce#2; make#12; stimulate#3}{create 2; make#13}{go 14; locomote#1; move#15; travel#4}{be 4; have the quality of being#1}
{human 1; individual#1; mortal#1; person#1; someone#1; soul#1}{animal 1; animate being#1; beast#1; brute#1; creature#1; fauna#1}{flora 1; plant#1; plant life#1}{matter 1; substance#1}{food 1; nutrient#1}{feeling 1}{act 1; human action#1; human activity#1}
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Explanations for low intersection of Base Concepts
• The individual selections are not representative enough.
• There are major differences in the way meanings are classified, which have an effect on the frequency of the relations.
• The translations of the selection to WordNet1.5 synsets are not reliable
• The resources cover very different vocabularies
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Concepts selected by at least two Concepts selected by at least two languages: intersections of pairslanguages: intersections of pairs
NOUNS
VERBS
NL ES IT EN NL ES IT EN
NL 1027 103 182 333 323 36 42 86
ES 103 523 45 284 36 128 18 43
IT 182 45 334 167 42 18 104 39
EN 333 284 167 1296 86 43 39 236
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Nouns Verbs Total
Physical objects & substances 491 491
Processes and states 272 228 500
Mental objects 33 33
Total 796 228 1024
Common Base Concepts
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Table 4: Number of Common BCs represented in the local wordnetsTable 4: Number of Common BCs represented in the local wordnets
Related to CBCs Eq_synonym Eq_near CBCs Without
Direct Equivalent
NL 992 725 269 97
ES 1012 1009 0 15IT 878 759 191 9
Table 5: BC4 Gaps in at least two wordnets (10 synsets)Table 5: BC4 Gaps in at least two wordnets (10 synsets)
body covering#1 mental object#1; cognitive content#1; content#2body substance#1 natural object#1social control#1 place of business#1; business establishment#1change of magnitude#1 plant organ#1contractile organ#1 plant part#1psychological feature#1 spatial property#1; spatiality#1
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Table 6: Local senses with complex equivalence Table 6: Local senses with complex equivalence relations to CBCsrelations to CBCs
NL ES ITEq_has_hyperonym 61 40 4eq_has_hyponym 34 14 20Eq_has_holonym 2 0Eq_has_meronym 3 2Eq_involved 3Eq_is_caused_by 3Eq_is_state_of 1
Example of complex relation
CBC: cause to feel unwell#1, Verb
Closest Dutch concept: {onwel#1}, Adjective (sick)
Equivalence relation: eq_is_caused_by
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EuroWordNet data Synsets No. of senses Sens./
syns. Entries Sens./
entry LIRels. LIRels/
syns EQRels-
ILI EQRels/s
yn Synsets without
ILI Dutch 44015 70201 1,59 56283 1,25 111639 2,54 53448 1,21 7203 Spanish 23370 50526 2,16 27933 1,81 55163 2,36 21236 0,91 0 Italian 40428 48499 1,20 32978 1,47 117068 2,90 71789 1,78 1561 French 22745 32809 1.44 18777 1.75 49494 2.18 22730 1.00 20 German 15132 20453 1.35 17098 1.20 34818 2.30 16347 1.08 0 Czech 12824 19949 1.56 12283 1.62 26259 2.05 12824 1.00 0 Estonian 7678 13839 1.80 10961 1.26 16318 2.13 9004 1.17 0 English 16361 40588 2,48 17320 2,34 42140 2,58 n.a. n.a. n.a. WN15 94515 187602 1,98 126617 1,48 211375 2,24 n.a. n.a. n.a.
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From EuroWordNet to Global WordNet
• Currently, wordnets exist for more than 50 languages, including:
• Arabic, Bantu, Basque, Chinese, Bulgarian, Estonian, Hebrew, Icelandic, Japanese, Kannada, Korean, Latvian, Nepali, Persian, Romanian, Sanskrit, Tamil, Thai, Turkish, Zulu...
• Many languages are genetically and typologically unrelated
• http://www.globalwordnet.org
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Global Wordnet Association
• Danish
• Norway
• Swedish
• Portuguese
• Korean
• Russian
• Basque
• Catalan
• Thai
Arabic Polish Welsh Chinese 20 Indian
Languages Brazilian
Portuguese Hebrew Latvian Persian Kurdish Avestan Baluchi Hungarian
• English
• German
• Spanish
• French
• Italian
• Dutch
• Czech
• Estonian
Romanian Bulgarian Turkish Slovenian Greek Serbian
EuroWordNet BalkaNet
http://www.globalwordnet.org
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Some downsides of the EuroWordnet model
• Construction is not done uniformly• Coverage differs• Not all wordnets can communicate with one
another• Proprietary rights restrict free access and usage• A lot of semantics is duplicated• Complex and obscure equivalence relations due to
linguistic differences between English and other languages
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Inter-LingualOntology
Device
Object
TransportDeviceEnglish Words
vehicle
car train
1
2
3 3
Czech Words
dopravní prostředník
auto vlak
2
1French Words
véhicule
voiture train
2
1
Estonian Words
liiklusvahend
auto killavoor
2
1
German Words
Fahrzeug
Auto Zug
2
1
Spanish Words
vehículo
auto tren
2
1
Italian Words
veicolo
auto treno
2
1
Dutch Words
voertuig
auto trein
2
1
Next step: Global WordNet Grid
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GWNG: Main Features
• Construct separate wordnets for each Grid language
• Contributors from each language encode the same core set of concepts plus culture/language-specific ones
• Synsets (concepts) can be mapped crosslinguistically via an ontology
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The Ontology: Main Features
• Formal ontology serves as universal index of concepts
• List of concepts is not just based on the lexicon of a particular language (unlike in EuroWordNet) but uses ontological observations
• Ontology contains only upper and mid-level concepts
• Concepts are related in a type hierarchy• Concepts are defined with axioms
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The Ontology: Main Features
• In addition to high-level (“primitive”) concept ontology needs to express low-level concepts lexicalized in the Grid languages
• Additional concepts can be defined with expressions in Knowledge Interchange Format (KIF) based on first order predicate calculus and atomic element
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The Ontology: Main Features
• Minimal set of concepts (Reductionist view):– to express equivalence across languages– to support inferencing
• Ontology must be powerful enough to encode all concepts that are lexically expressed in any of the Grid languages
• Ontology need not and cannot provide a linguistic encoding for all concepts found in the Grid languages – Lexicalization in a language is not sufficient to warrant inclusion
in the ontology– Lexicalization in all or many languages may be sufficient
• Ontological observations will be used to define the concepts in the ontology
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Ontological observations• Identity criteria as used in OntoClean (Guarino &
Welty 2002), :– rigidity: to what extent are properties true for entities
in all worlds? You are always a human, but you can be a student for a short while.
– essence: what properties are essential for an entity? Shape is essential for a statue but not for the clay it is made of.
– unicity: what represents a whole and what entities are parts of these wholes? An ocean is a whole but the water it contains is not.
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Type-role distinction
• Current WordNet treatment:(1) a husky is a kind of dog(type)(2) a husky is a kind of working dog (role)• What’s wrong? (2) is defeasible, (1) is not:*This husky is not a dogThis husky is not a working dog
Other roles: watchdog, sheepdog, herding dog, lapdog, etc….
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Ontology and lexicon
•Hierarchy of disjunct types:Canine PoodleDog; NewfoundlandDog;
GermanShepherdDog; Husky
•Lexicon:– NAMES for TYPES:
{poodle}EN, {poedel}NL, {pudoru}JP((instance x Poodle)
– LABELS for ROLES:{watchdog}EN, {waakhond}NL, {banken}JP
((instance x Canine) and (role x GuardingProcess))
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Ontology and lexicon
•Hierarchy of disjunct types:River; Clay; etc…
•Lexicon:– NAMES for TYPES:
{river}EN, {rivier, stroom}NL((instance x River)
– LABELS for dependent concepts:{rivierwater}NL (water from a river => water is not a unit){kleibrok}NL (irregularly shared piece of clay=>non-essential) ((instance x water) and (instance y River) and (portion x y)((instance x Object) and (instance y Clay) and (portion x y)
and (shape X Irregular))
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Rigidity
• The “primitive” concepts represented in the ontology are rigid types
• Entities with non-rigid properties will be represented with KIF statements
• But: ontology may include some universal, core concepts referring to roles like father, mother
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Properties of the Ontology
• Minimal: terms are distinguished by essential properties only
• Comprehensive: includes all distinct concepts types of all Grid languages
• Allows definitions via KIF of all lexemes that express non-rigid, non-essential properties of types
• Logically valid, allows inferencing
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Mapping Grid Languages onto the Ontology
• Explicit and precise equivalence relations among synsets in different languages:– type hierarchy is minimal– subtle differences can be encoded in KIF expressions
• Grid database contains wordnets with synsets that label • --either “primitive” types in the hierarchies, • --or words relating to these types in ways made explicit in
KIF expressions • If 2 lgs. create the same KIF expression, this is a statement
of equivalence!
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How to construct the GWNG• Take an existing ontology as starting point;• Use English WordNet to maximize the number of disjunct
types in the ontology;• Link English WordNet synsets as names to the disjunct
types;• Provide KIF expressions for all other English words and
synsets• Copy the relation to the ontology to other languages,
including KIF statements built for English• Revise KIF statements to make the mapping more precise• Map all words and synsets that are and cannot be mapped
to English WordNet to the ontology:– propose extensions to the type hierarchy– create KIF expressions for all non-rigid concepts
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Initial Ontology: SUMO (Niles and Pease)
SUMO = Suggested Upper Merged Ontology
--consistent with good ontological practice
--fully mapped to WordNet(s): 1000 equivalence mappings, the rest through subsumption
--freely and publicly available
--allows data interoperability
--allows NLP
--allows reasoning/inferencing
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SUMO
• 1,000 generic, abstract, high-level terms
• 4,000 definitional statements
• MILO (Mid-Level Ontology)
closer to lexicon, WordNet
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Mapping Grid languages onto the Ontology
• Check existing SUMO mappings to Princeton WordNet -> extend the ontology with rigid types for specific concepts
• Extend it to many other WordNet synsets• Observe OntoClean principles! (Synsets
referring to non-rigid, non-essential, non-unicitous concepts must be expressed in KIF)
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Lexicalizations not mapped to WordNet• Not added to the type hierarchy:
{straathond}NL (a dog that lives in the streets)((instance x Canine) and (habitat x Street))
• Added to the type hierarchy:{klunen}NL (to walk on skates from one frozen body to
the next over land)WalkProcess KluunProcessAxioms:(and (instance x Human) (instance y Walk) (instance z
Skates) (wear x z) (instance s1 Skate) (instance s2 Skate) (before s1 y) (before y s2) etc…
• National dishes, customs, games,....
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Most mismatching concepts are not new types
• Refer to sets of types in specific circumstances or to concept that are dependent on these types, next to {rivierwater}NL there are many other:
{theewater}NL (water used for making tea)
{koffiewater}NL (water used for making coffee)
{bluswater}NL (water used for making extinguishing file)
• Relate to linguistic phenomena:– gender, perspective, aspect, diminutives, politeness,
pejoratives, part-of-speech constraints
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• {teacher}EN((instance x Human) and (agent x
TeachingProcess))
• {Lehrer}DE ((instance x Man) and (agent x TeachingProcess))
• {Lehrerin}DE ((instance x Woman) and (agent x TeachingProcess))
KIF expression for gender marking
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KIF expression for perspective
sell: subj(x), direct obj(z),indirect obj(y) versus buy: subj(y), direct obj(z),indirect obj(x) (and (instance x Human)(instance y Human)
(instance z Entity) (instance e FinancialTransaction) (source x e) (destination y e) (patient e)
The same process but a different perspective by subject and object realization: marry in Russian two verbs, apprendre in French can mean teach and learn
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Aspectual variants
• Slavic languages: two members of a verb pair for an ongoing event and a completed event.
• English: can mark perfectivity with particles, as in the phrasal verbs eat up and read through.
• Romance languages: mark aspect by verb conjugations on the same verb.
• Dutch, verbs with marked aspect can be created by prefixing a verb with door: doorademen, dooreten, doorfietsen, doorlezen, doorpraten (continue to breathe/eat/bike/read/talk).
• These verbs are restrictions on phases of the same process• Does NOT warrant the extension of the ontology with
separate processes for each aspectual variant
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Kinship relations in Arabic
• (~Eam)َع6م father's brother, paternal uncle.
• (xaAl) َخ6ال mother's brother, maternal uncle.
• (Eam~ap) َع6َّم>ة father's sister, paternal aunt.
• اَل6ة (xaAlap) َخ6 mother's sister, maternal aunt
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Kinship relations in Arabic
• .........• َقAيَق6ة sister, sister on the paternal (aqiyqapfull$) َش6
and maternal side (as distinct from تDَخE :(uxot<) ُأ'sister' which may refer to a 'sister' from paternal or maternal side, or both sides).
• 6ْكDالن (vakolAna) َث father bereaved of a child (as opposed to يمA 6ِت Aيَّم6ة or (yatiym) َي 6ِت for (yatiymap) َيfeminine: 'orphan' a person whose father or mother died or both father and mother died).
• Dَل6ى 6ْك (vakolaYa) َث other bereaved of a child (as opposed to يمA 6ِت Aيَّم6ة or َي 6ِت for feminine: 'orphan' a َيperson whose father or mother died or both father and mother died).
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father's brother, paternal uncle
WORDNETpaternal uncle => uncle
=> brother of ....????
ONTOLOGY(=> (paternalUncle ?P ?UNC) (exists (?F) (and (father ?P ?F) (brother ?F ?UNC))))
Complex Kinship concepts
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Universality as evidence
• English verb cut abstracts from the precise process but there are troponyms that implicate the manner :– snip, clip imply scissors, chop and hack a large knife or an axe
• Dutch there is no general verb but only specific verbs:knippen “clip, snip, cut with scissors or a scissor-like tool'”, snijden
“cut with a knife or knife-like tool”, hakken “chop, hack, to cut with an axe, or similar tool”).
• If lexicalization of the specific process is more universal it can be seen as evidence that the specific processes should be listed in the ontology and not the generic verb
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Open Questions/Challenges
• What is a word, i.e., a lexical unit?
• What is the status of complex lexemes like English lightning rod, word of mouth, find out, kick the bucket?
• What is a semantic unit, i.e. a concept?
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Open Questions/Challenges
• Is there a core inventory of concepts that are universally encoded?
• If so, what are these concepts?• How can crosslinguistic equivalence be verified?• Is there systematicity to the language-specific
extensions?• What are the lexicalization patterns of individual
languages? • Are lexical gaps accidental or systematic?
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Coverage: what belongs in a universal lexical database?
• Formal, linguistic criteria for inclusion
• Informal, cultural criteria
• Both are difficult to define and apply!
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Advantages of the Global Wordnet Grid
• Shared and uniform world knowledge:– universal inferencing– uniform text analysis and interpretation
• More compact and less redundant databases• More clear notion how languages map to
the knowledge – better criteria for expressing knowledge– better criteria for understanding variation
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dog
watchdog
poodlestreet dog
dachshundlapdog
short hair dachshund
long hairdachshund
Expansion from a type to roles
hunting dog
Expansion with pure hyponymy relations
puppy
bitch
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dog
watchdog
poodlestreet dog
dachshundlapdog
short hair dachshund
long hair dachshund
Expansion from a role to types and other roles
hunting dog
Expansion with pure hyponymy relations
puppy
bitch
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Automotive ontology: (http://www.ontoprise.de)
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Who uses ontologies?
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