Download ppt - The Global Wordnet Grid: anchoring languages to universal meaning Piek Vossen Irion Technologies/Vrije Universiteit Amsterdam 6 th International Plain

The Global Wordnet Grid: anchoring languages to universal meaning

Piek Vossen

Irion Technologies/Vrije Universiteit Amsterdam

6th International Plain Language Conference, October 11-14th, 2007, Amsterdam

6th International PLAIN language Conference

11-14th October, Amsterdam

Overview: Problem: effective language and communication

From human to human From human to machine From machine to machine From human to machine and back to human, maybe via other

machines... Solution: anchoring language to universal meaning

Wordnets: network of words related through meaning The Global Wordnet Grid: wordnets for languages connected

to each other through an ontology Future:

Equal access to the knowledge and information on the Internet to all people, regardless of language and background

Systems that start to understand language



Problem



Language is inherently vague and ambiguous Communication through language:

mediates between the expectation of the Speaker and the Hearer => half a word is enough

Language is not fully descriptive but minimally sufficient: Do not bother the Hearer with information that is

already known => rely on background knowledge Use a minimal set of words and expressions to

avoid memory overloading => words and expressions have multiple meaning



Concept in our head

Plato with beard

"gavagai"

W.V.O.Quine (1964): inscrutability of reference

rabbit with carrots and rosemary

devine appearance announcing spring

sweet pet wanna hug

Understanding is fundamentally impossible



Full understanding is fundamentally impossible BUT? People do communicate... People even communicate with computers... As long as language is effective:

meaning= to have the desired effect! Link language to useful content!



What is effective computer-mediated language? Computers store information and knowledge in

textual form: People search information and knowledge by 'querying'

computers Effective Computer Mediated Communication (CMC) = find

what you need and nothing else Computers analyze information and knowledge:

Collect data and send alerts, reports and facts Computers connect people:

Support communication across people by analyzing communication or translating languages



Strings Strings

Expression in language

Words….


….Words

Strings

Concept

Query

Concept

InformationSeeker

InformationProvider

Information

ape

….

energy

….

mass

….

….

zebra

Index of Strings



Strings Strings


my cell phone….


….mobile

Strings

Concept Concept

Query Information

InformationSeeker

InformationProvider

ape

….

….

….

mobile

….

….

zebra

Index of Strings

Conceptual match

Linguistic mismatch



Strings Strings


my cell phone….


….nerve cells

Strings

Concept Concept

Query Information

InformationSeeker

InformationProvider

ape

….

cell

….

….

….

….

zebra

Index of Strings

Conceptual mismatch

Linguistic match



Strings Strings


police cell ….


…. nerve cells

Strings

Concept Concept

Query Information

InformationSeeker

InformationProvider

ape

….

cell

….

….

….

….

zebra

Index of Strings

Conceptual mismatch

Linguistic match



Strings Strings


neuron ….


….nerve cells

Strings

Concept Concept

Query Information

InformationSeeker

InformationProvider

ape

….

cell

….

….

….

….

zebra

Index of Strings

Conceptual match

Linguistic mismatch



Recall & Precision

query:

“cell”

Search engine for

database with

all documents

“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)



Useless dialogues with Alice-bot



It is useful to anchor meaning!

Anchoring already takes place all over the world through standardization:

measures and units: meter, liter, kilo terminological databases, legal definitions,

contracts international cooperation ontologies: definition of the meaning of concepts

in a formal knowledge presentation system, (1st order logic) so that a computer can reason with it



Solution



How can we anchor the meaning of words? We can anchor words to each other:

semantic network or wordnet We can anchor words to logical implications:

a formal ontology



Relational model of meaning

man woman

boy girl

cat

kitten

dog

puppy

animal

man

woman

boy

meisje

cat

kitten

dogpuppy

animal



Princeton WordNet

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 100,000 concepts and over

120,000 English words



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}

ENGLISHCar…

Train…

Vehicle

Inter-Lingual-Index

Transport

Road Air Water

Domains DOLCESUMO

Device

Object

TransportDevice

English Words

vehicle

car train

1

2

4

3 3

Czech Words

dopravní prostředník

auto vlak

2

1

French 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

Wordnet familyPrinceton WordNet, (Fellbaum 1998): 115,000 concepsEuroWordNet, (Vossen 1998): 8 languagesBalkaNet, (Tufis 2004): 6 languagesGlobal Wordnet Association: all languages



Wordnets as autonomous language-specific 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



Complex equivalence Complex equivalence relationsrelations1. 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 Target record: decoration.

3. Multiple Targets and Sources (many:many)Dutch wordnet: toestel near_synonym apparaat

Target records: machine; device; apparatus; tool



Gaps in the English WordNet:

genuine, cultural gaps: unknown in English culture:

Dutch: klunen, to walk on skates over land from one frozen water to the other

pragmatic gaps: the concept is known but is not expressed by a single lexicalized form in English:

Dutch: kunstproduct = artifact substance <=> artifact object

Complex equivalece relationsComplex equivalece relations



From EuroWordNet to Global WordNet Global Wordnet Association:

http://www.globalwordnet.org Bi-annual conference: India (2002), Czech (2004),

Korea (2006), Hungary (2008), .... Currently, wordnets exist for more than 40

languages, including:Arabic, Bantu, Basque, ...., Chinese, Bulgarian, Estonian, Hebrew, ...., Icelandic, Japanese, Kannada, Korean, Latvian, Latin, ....Nepali, Persian, Romanian, Sanskrit, Tamil, Thai, Turkish, .... Zulu

Many languages are genetically and typologically unrelated



Some downsides

Construction is not done uniformly Coverage differs Not all Wordnets can communicate with one another:

not linked linked to different versions: 1.5, 1.6, 1.7, 2.0 and now 3.0, 3.1 linked with different relations

Proprietary rights restrict free access and usage A lot of the semantics is duplicated Complex and obscure equivalence relations due to

linguistic differences between English and other languages



Inter-LingualOntology

Device

Object

TransportDeviceEnglish Words

vehicle

car train

1

2

3 3

Czech Words

dopravní prostředník

auto vlak

2

1

French 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



The Ontology: main features Formal, artificial 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:

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

Concepts are related in a type hierarchy Concepts are defined with axioms: Knowledge

Interchange Format (KIF) based on first order predicate calculus and atomic elements



Concepts by ontological observations

Types and Roles among the hyponyms of dog in Wordnet: husky, lapdog; toy dog; hunting dog; working dog; dalmatian,

coach dog, carriage dog; basenji; pug, pug-dog; Leonberg; Newfoundland; Great Pyrenees; spitz; griffon, Brussels griffon, Belgian griffon; corgi, Welsh corgi; poodle, poodle dog; Mexican hairless; pooch, doggie, doggy, barker, bow-wow; cur, mongrel, mutt

Current WordNet treatment:(1) a husky is a kind of dog(2) a husky is a kind of working dog

What’s wrong? (2) is defeasible, (1) is not:*This husky is not a dog => RIGID TYPEThis husky is not a working dog => ROLE, NON-RIGID



Ontology versus wordnet

Hierarchy of disjunct types:Canine PoodleDog; NewfoundlandDog;

GermanShepherdDog; Husky

Wordnet: 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))



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 words that express non-rigid, non-essential properties of types

Logically valid, allows inferencing



Ontology versus 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)KluunProcess => WalkProcessAxioms:(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,....



Ontology versus Wordnet

Refer to sets of types in specific circumstances or to concept that are dependent on these types, next to {rivierwater}NL there are many others:

{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



{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



KIF expression for perspective

sell: subj(x), direct obj(z),indirect obj(y) buy: subj(y), direct obj(z),indirect obj(x)

FinancialTransaction(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



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



Future



Synonyms,Semantic network

thesaurus

golfclub(s)

Tiger

Woods

golf

sticks

Language technology: a hole in one!

golfclubs

Linguistic analysis

Golf at the club

clubs

for golf



Index concepts rather than words

Meaning of a word in context: Domain of the document:

Juventus => football Topic of the paragraph:

transfer scandal => business, crime Phrase: linguistically-motivated combination of

words: [wing player]football player in [police cell]jail

Topic of the query: Can I order chicken wings? => food

Phrase: [chicken wings]dish



dog

watchdog

poodlestreet dog

dachshundlapdog

short hair dachshund

long hairdachshund

Expansion from a type to roles

hunting dog

Expansion with clear hyponymy

puppy

bitch



dog

watchdog

poodlestreet dog

dachshundlapdog

short hair dachshund

long hair dachshund

Expansion from a role to types and other roles

hunting dog

Expansion with clear hyponymy

puppy

bitch



Ontology

Texts

Objectsin reality

Thought

Expression

携帯電話(keitaidenwa )

Knowledge &information

Useful and effective behavior:-reason over knowledge-collect information and data-deliver services and be helpful



Automotive ontology: (http://www.ontoprise.de)



Dialogue system

DialogueManager

• Can I help you?• My head phone is broke.

• I want to buy a new one.

• Would you like repair or products?

• Can yousay more about products?

• It is for my cell phone.

• Can you give more details?

• It is a Nokia 6110• I got the following accessoires for you. Please have a look.

UserModel-Intention-Satisfaction-Emotion

InformationState:-Positive-Negative-Relations

• That is not what I want!

QuestionAnalysis

Topicdetection

SearchEngine

reparair

information

accessories

products

Website

TextAnalysis

Word

mobile

head phone

Concept



Prevent deadlocks: Detects vagueness and ambiguity (what meaning of cell?) Detect topic changes Uses negative feedback: “No jails, I want cell phones!”

Can handle out-of-domain questions (users do not know what the system knows) :

"We do not have hotel rooms but we do have electronic equipment".

"No, we do not have portophones but we do have other electronic equipement such as cell phones"

Communicative dialog system

hotel room

room

space

equipment

cell phone portophone

object



THANK YOU FOR YOUR ATTENTION