building wordnets piek vossen, irion technologies
TRANSCRIPT
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Building Wordnets
Piek Vossen, Irion Technologies
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Overview
Starting points Semantic framework Process overview Methodologies in other projects Multilinguality
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Starting points
Purpose of the wordnet database: education, science, applications formal ontology or linguistic ontology making inferences or lexical substitution conceptual density or large coverage
Distributed development Reproducability Available resources Language-specific features (Cross-language) compatibility Exploit cummunity resources by projecting
conceptual relations on a target wordnet
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Semantic framework
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Differences in wordnet structures
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
- Artificial Classes versus Lexicalized Classes: instrumentality; natural object
- Lexicalization differences of classes: container and artifact (object) are not lexicalized in Dutch
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Linguistic versus conceptual ontologies
Conceptual ontology: A particular level or structuring may be required to achieve a 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 ontology: Exactly reflects the relations between all the lexicalized words and expressions in a language. 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,
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Wordnets as Linguistic Ontologies
Classical Substitution Principle:
Any word that is used to refer to something can be replaced by its synonyms, hyperonyms and
hyponyms:
horse stallion, mare, pony, mammal, animal, being.
It cannot be referred to by co-hyponyms and co-hyponyms of its hyperonyms:
horse X cat, dog, camel, fish, plant, person, object.
Conceptual Distance Measurement:
Number of hierarchical nodes between words is a measurement of closeness,
where the level and the local density of nodes are additional factors.
Main purpose is to predict what words can be used as substitutes in language, considering all the lexicalized words in a language.
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Define a semantic framework
Definition of relations Diagnostic frames (Cruse 1986) Examples and corpus data
Top-level ontology Constraints on relations Type consistency Large scale validation
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Process overview
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Techniques
Manual encoding and verification Automatic extraction:
definitions synonyms distribution and similarity patterns in copora defining contexts, e.g. “cats and other pets” parallel corpora, e.g. bible translations morphological structure bilingual dictionaries
Encode source and status of data: who, when, based on what algorithm, validated, final
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Encoding cycle
1. Collecting data Vocabulary: what is the list of words of a language? Concepts: what is the list of concepts related to the
vocabulary? 2. Encoding data:
Defining synsets Defining language internal relations: hyponymy, meronymy
roles, causal relations Defining equivalence relations to English Defining other relations,e.g. Ontology types, Domains
3. Validation 4. Go to 1.
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Where to start?
How to get a first selection: Words (alphabetic, frequency) -> concepts -> relations Concept (hyperonym, domain, semantic feature) -> words -
> concepts -> relations How to get a complete overview of words and
expressions that belong to a segment of a wordnet? Up to 20 hyperonyms for instrumentality: instrument,
instrumentality, means, tool, device, machine, apparatus, ....
iterative process: collect, structure, collect, restructure... using multiple sources of evidence comparing results, e.g. tri-cycle is a toy or a vehicle
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Synonymy as a basis?
Synsets are the core unit of a wordnet database Synonymy is only vaguely defined: substitution in a
context. Synonyms are very hard to detect Other relations (role relations, causal relations):
easier to detect and encode easier to validate within a formal framework easier to validate in a corpus
Rich set of relations per concept help alignment with other resources
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Diagnostic frames and examplesAgent Involvement(A/an) X is the one/that who/which does the Y, typically intentionally.Conditions: - X is a noun
- Y is a verb in the gerundive formExample:
A teacher is the one who does the teaching intentionallyEffect:
{to teach} (Y) INVOLVED_AGENT {teacher} (X)
Patient Involvement(A/an) X is the one/that who/which undergoes the YConditions: - X is a noun
- Y is a verb in the gerundive formExample:
A learner is the one who undergoes the learningEffect:
{to learn} (Y) INVOLVED_PATIENT {learner} (X)
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Diagnostic frames and examplesResult Involvement
A/an) X is comes into existence as a result of Y, where X is a noun and Y is a verb in the gerundive form and a hyponym of “make”, “produce”, “generate”.
Example:A crystal comes into existence as a result of crystalizingA crystal is the result of crystalizingA crystal is created by crystalizing
Effect:{to crystalize} (Y) INVOLVED_RESULT {crystal} (X)
Comments: Special kind of patient relation. The entity is not jut changed or
affected but it comes into existence as a result of the event: Only applies to concrete entities (1stOrder) or mental objects such as
ideas (3rdOrder). Situations that result from other situations are related by the CAUSE
relation.
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Hyponymy overloading (Guarino 1998, Vossen and Bloksma
1998). The vocabulary does not clearly differentiate between orthogonal roles and disjoint types: role: passenger, teacher, student type: dog; cat ?:
knife ->weapon, cutlery; spoon -> container, cutlery food material <- building material <-?- stone; <-?-water; <- brick;
Disjunctive and conjunctive hyperonyms: albino -> animal or plant spoon -> cutlery & container
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Hyponymy restructuring
dierenziekte(animal disease)
infectieziekte(infectious disease)
ingewandsziekte(bowel disease)
ziekte (disease)
kolder(staggers: brain disease of cattle)
vuilbroed(infectious infectious
disease of bees)
veeziekte(cattle disease)
haringwormziekte(anisakiasis: bowel disease of herrings)
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Methodologies in a number of projects Princeton Wordnet EuroWordNet:
English, Dutch, German, French, Spanish, Italian, Czech, Estonian
10,000 up to 50,000 synsets BalkaNet:
Romanian, Bulgarian, Turkish, Slovenian, Greek, Serbian
10,000 synsets
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Main strategies for building wordnets Expand approach: translate WordNet synsets to another
language and take over the structure easier and more efficient method compatible structure with WordNet vocabulary and structure is close to WordNet but also biased can exploit many resources linked to Wordnet: SUMO, Wordnet
domains, selection restriction from BNC, etc...
Merge approach: create an independent wordnet in another language and align it with WordNet by generating the appropriate translations
more complex and labor intensive different structure from WordNet language specific patterns can be maintained, i.e. very precise
substitution patterns
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Aligning wordnetsAligning wordnets
muziekinstrument
orgel
hammond orgel
organ ? organ organ
hammond organ
musical instrument
instrument
artifact object natural object
object
Dutch wordnet English wordnet
orgaan
orgel?
?
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General criteria for approach: Maximize the overlap with wordnets for other
languages Maximize semantic consistency within and
across wordnets Maximally focus the manual effort where
needed Maximally exploit automatic techniques
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Top-down methodology Develop a core wordnet (5,000 synsets):
all the semantic building blocks or foundation to define the relations for all other more specific synsets, e.g. building -> house, church, school
provide a formal and explicit semantics Validate the core wordnet:
does it include the most frequent words? are semantic constraints violated?
Extend the core wordnet: (5,000 synsets or more): automatic techniques for more specific concepts with high-
confidence results add other levels of hyponymy add specific domains add ‘easy’ derivational words add ‘easy’ translation equivalence
Validate the complete wordnet
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Developing a core wordnet Define a set of concepts(so-called Base Concepts) that play an
important role in wordnets: high position in the hierarchy & high connectivity represented as English WordNet synsets Common base concepts: shared by various wordnets in different
languages Local base concepts: not shared
EuroWordNet: 1024 synsets, shared by 2 or more languages BalkaNet: 5000 synsets (including 1024) Common semantic framework for all Base Concepts, in the form of a
Top-Ontology Manually translate all Base Concepts (English Wordnet synsets) to
synsets in the local languages (was applied for 13 Wordnets) Manually build and verify the hypernym relations for the Base
Concepts All 13 Wordnets are developed from a similar semantic core closely
related to the English Wordnet
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63TCs
1024 CBCs
First Level Hyponyms
Remaining Hyponyms
Hyperonyms
CBCRepresen- tatives
Local BCs
WMsrelated vianon-hyponymy
Top-Ontology
Inter-Lingual-Index
Remaining Hyponyms
Hyperonyms
CBCRepre-senta.
Local BCs
WMsrelated vianon-hyponymyFirst Level Hyponyms
RemainingWordNet1.5Synsets
Top-down methodology
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DomainNamedEntities
Next Level Hyponyms
SumoOntology
WordNetSynsets
SBC
Hypernyms
ABCEuroWordNet BalkaNetBase Concepts
5000Synsets
EnglishArabic
Lexiconteach
-darrasa
WordNet Domains
Domain“chemics”
WordNetSynsets
English Wordnet Arabic Wordnet
Arabicword
frequency
Arabicroots
&derivation
rules
Top-down methodology
More Hyponyms
EasyTranslations
NamedEntities
1000Synsets
=
Core wordnet5000 synsets
CBC
WordNetSynsets
1045678-v{teach}
WordNetSynsets
1045678-v{darrasa}
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Advantages of the approach
Well-defined semantics that can be inherited down to more specific concepts Apply consistency checks Automatic techniques can use semantic basis
Most frequent concepts and words are covered
High overlap and compatibility with other wordnets
Manual effort is focussed on the most difficult concepts and words
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Distribution over the top ontology clusters
WN NL ES IT Top-Concept TC-
Tokens %of wn
TC-Tokens
% of nl
%of wn
TC-Tokens
%of es %of wn
TC-Tokens
%of it %of wn
Animal 14068 3.99% 1193 0.97% 8.5% 2458 1.81% 17.5% 1122 1.44% 8.0% Artifact 19562 5.55% 10803 8.83% 55.2% 9969 7.36% 51.0% 6494 8.34% 33.2% Building 1022 0.29% 707 0.58% 69.2% 628 0.46% 61.4% 434 0.56% 42.5% Comestible 3377 0.96% 1393 1.14% 41.2% 1614 1.19% 47.8% 624 0.80% 18.5% Container 1725 0.49% 778 0.64% 45.1% 799 0.59% 46.3% 432 0.55% 25.0% Covering 2030 0.58% 1208 0.99% 59.5% 1027 0.76% 50.6% 690 0.89% 34.0% Creature 664 0.19% 159 0.13% 23.9% 254 0.19% 38.3% 27 0.03% 4.1% Function 34081 9.68% 17668 14.44% 51.8% 18904 13.96% 55.5% 11043 14.18% 32.4% Furniture 298 0.08% 171 0.14% 57.4% 147 0.11% 49.3% 87 0.11% 29.2% Garment 756 0.21% 494 0.40% 65.3% 426 0.31% 56.3% 292 0.37% 38.6% Gas 93 0.03% 67 0.05% 72.0% 62 0.05% 66.7% 49 0.06% 52.7% Group 27805 7.90% 3357 2.74% 12.1% 3630 2.68% 13.1% 2337 3.00% 8.4% Human 11543 3.28% 6372 5.21% 55.2% 7683 5.67% 66.6% 4488 5.76% 38.9% ImageRepresentation 780 0.22% 412 0.34% 52.8% 426 0.31% 54.6% 294 0.38% 37.7% Instrument 7036 2.00% 4102 3.35% 58.3% 3590 2.65% 51.0% 2564 3.29% 36.4% LanguageRepresent. 2844 0.81% 1273 1.04% 44.8% 1218 0.90% 42.8% 691 0.89% 24.3% Liquid 1629 0.46% 617 0.50% 37.9% 500 0.37% 30.7% 339 0.44% 20.8% Living 47104 13.37% 10225 8.36% 21.7% 13661 10.08% 29.0% 7408 9.51% 15.7%
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Wordnet Domains Concepts Proportion
Wordnet Domains Concepts Proportion
acoustics 104 0.092% linguistics 1545 1.363%
administration 2974 2.624% literature 686 0.605%
aeronautic 154 0.136% mathematics 575 0.507%
agriculture 306 0.270% mechanics 532 0.469%
alimentation 28 0.025% medicine 2690 2.374%
anatomy 2705 2.387% merchant_navy 485 0.428%
anthropology 896 0.791% meteorology 231 0.204%
applied_science 28 0.025% metrology 1409 1.243%
archaeology 68 0.060% military 1490 1.315%
archery 5 0.004% money 624 0.551%
architecture 255 0.225% mountaineering 28 0.025%
art 420 0.371% music 985 0.869%
artisanship 148 0.131% mythology 314 0.277%
astrology 17 0.015% number 220 0.194%
astronautics 29 0.026% numismatics 43 0.038%
astronomy 376 0.332% occultism 52 0.046%
athletics 22 0.019% oceanography 10 0.009%
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EWN Interlingual RelationsEWN Interlingual Relations
• EQ_SYNONYM: there is a direct match between a synset and an ILI-record
• EQ_NEAR_SYNONYM: a synset matches multiple ILI-records simultaneously,
• HAS_EQ_HYPERONYM: a synset is more specific than any available ILI-record.
• HAS_EQ_HYPONYM: a synset can only be linked to more specific ILI-records.
• other relations: CAUSES/IS_CAUSED_BY, EQ_SUBEVENT/EQ_ROLE, EQ_IS_STATE_OF/EQ_BE_IN_STATE
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Multilinguality
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Complex equivalence relationsComplex equivalence relations
eq_near_synonym1. Multiple Targets
One sense for Dutch schoonmaken (to clean) which simultaneously matches with at least 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)}
The Dutch synset schoonmaken will thus be linked with an eq_near_synonym relation to all these sense of clean.
2. Multiple Source meaningsSynsets inter-linked by a near_synonym relation can be linked to same target ILI-record(s), either with an eq_synonym or an eq_near_synonym relation:
Dutch wordnet: toestel near_synonym apparaatILI-records: {machine}; {device}; {apparatus}; {tool}
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Complex equivalence relationsComplex equivalence relations
has_eq_hyperonym Typically used for gaps in WordNet1.5 or in English:
• genuine, cultural gaps for things not known in English culture, e.g. citroenjenever, which is a kind of gin made out of lemon skin, • pragmatic, in the sense that the concept is known but is not expressed by a single lexicalized form in English, e.g.: Dutch hoofd only refers to human head and Dutch kop only refers to animal head, English uses head for both.
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 can be used to refer to both finger and toe.
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Overview of equivalence relations to the ILI
Relation POS Sources: Targets Exampleeq_synonym same 1:1 auto : voiture
careq_near_synonym any many : many apparaat, machine, toestel:
apparatus, machine, deviceeq_hyperonym same many : 1 (usually) citroenjenever:
gineq_hyponym same (usually) 1 : many dedo :
toe, fingereq_metonymy same many/1 : 1 universiteit, universiteitsgebouw:
universityeq_diathesis same many/1 : 1 raken (cause), raken:
hiteq_generalization same many/1 : 1 schoonmaken :
clean
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Filling gaps in the ILI
Types of GAPS 1. genuine, cultural gaps for things not known in English culture,
e.g. citroenjenever, which is a kind of gin made out of lemon skin,
• Non-productive• Non-compositional
2. pragmatic, in the sense that the concept is known but is not expressed by a single lexicalized form in English, e.g.: container, borrower, cajera (female cashier)
• Productive• Compositional
3. Universality of gaps: Concepts occurring in at least 2 languages
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Productive and Predictable Lexicalizations exhaustively linked to the ILI beat
stamp
{doodslaanV}NL
{cajeraN}ES
{doodschoppenV}NL
{doodstampenV}NL
kill
kick
{tottrampelnV}DE
{totschlagenV}DE
hypernym
cashier
female
young
fish
{casière}NL
{alevínN}ES
in_state
in_state
in_state
hypernym
hypernym
hypernym
hypernym
hypernym
hypernym
hypernym
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DomainNamedEntities
Next Level Hyponyms
SumoOntology
WordNetSynsets
1000Synsets
SBCCBC
Hypernyms
ABCEuroWordNet BalkaNetBase Concepts
5000Synsets
EnglishArabic
LexiconWordNet Domains
Domain“chemics”
WordNetSynsets
English Wordnet Arabic Wordnet
Arabicword
frequency
Arabicroots
&derivation
rules
Top-down methodology
More Hyponyms
EasyTranslations
NamedEntities
=
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dierenziekte(animal disease)
infectieziekte(infectious disease)
ingewandsziekte(bowel disease)
ziekte (disease)
kolder(staggers: brain disease of cattle)
vuilbroed(infectious infectious
disease of bees)
veeziekte(cattle disease)
haringwormziekte(anisakiasis: bowel disease of herrings)
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dierenziekte(animal disease)
infectieziekte(infectious disease)
ingewandsziekte(bowel disease)
ziekte(disease)
kolder(staggers: brain disease of cattle)
vuilbroed(infectious infectious
disease of bees)
veeziekte(cattle disease)
haringwormziekte(anisakiasis: bowel disease of herrings)
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Resources
Monolingual dictionaries: definitions synonym relations other relations
Bi-lingual dictionaries: L-English, English-L Ontologies Thesauri Corpora:
monolingual parallel