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Lexicons and Complex
Expressions towards Multilingual Linking
Nicoletta Calzolari Copenhagen, October 2001
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What is SIMPLE?
A set of 12 harmonised computational lexicons for
HLT applications, geared for multilingual links

• A common
• rich model
• representation language
• methodology of building the lexicon
• common Template Types, with default obligatory
  info (Type defining), and indication of optional
  info
• First time on a large scale, for so many
  languages
• Lexical meaning represented in terms of
  integrated combinations of different sorts of
  information (semantic type, argument structure,
  relations, features, etc. )
• Ontology-based information comes together with
  predicative representation and syntactic linking
• A shared set of SemUs (from EWN) (about 700) of
  the 12 Lexicons cross-lingually related

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PAROLE/SIMPLE Architecture CLIPS Italian
National Project
60,000 lemmas
55,000 lemmas
55,000 SemU
MuS
SynU
Sem Info
Sem Info
Sem Info
Sem Info
TEMPLATE
Lexical Rel
Sem. Rel
Sem. Feat
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Semantic information in SIMPLE
Word senses encoded as Semantic Units (SemUs),
containing the following info

• Semantic type
• Domain
• Lexicographic gloss
• Extdended Qualia structure
• Reg. Polysemy altern.
• Event type
• Derivation relations
• Synonymy
• Collocations

• Argument structure for predicative SemUs
• Selection restrictions on the arguments
• Link of the arguments to the syntactic
  subcategorization frames (represented in the
  PAROLE lexicons)

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Semantic Multidimensionality and NLP
NLP tasks (IE, WSD, NP Recognition, etc.) need to
access multidimensional aspects of word meaning,
represented in SIMPLE with the Extended Qualia
Relations
Is_a_part_of
Member_of
la pagina del libro (the page of the book) il
difensore della Juventus (Juventus fullback) il
suonatore di liuto (the lute player) il tavolo di
legno (the wooden table)
Telic
Made_of
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Overall Organization

...
Greek lexicon
Danish lexicon
Type Ontology ?150 types
Catalan lexicon
Template
Instantiation
Italian lexicon
Pred. Layer

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Semantic Information
The SIMPLE Way
The Core Ontology represents a first level of
organization of the semantic type system
Each type is associated to a Template consisting
of a cluster of information (relations,
features, argument structure, event type, etc.)
that defines the type
The information characterizing a Semantic Unit
includes
a. The type defining information (associated
to the template the SemU instantiates)
b. Additional information (other relations or
features, selectional restrictions, terminology,
cross-part of speech relations, polysemy, etc.)
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Template
redundancy
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Verb Examples hear, smell, etc. Noun
Examples sight, look, etc. Linguistic
Tests . Levin Class 30.1 (See verb, e.g.
detect, see, notice), 30.4 (Stimulus subject,
e.g. look,
smell) Comments Processes involving an
experiencing relation, . SemU
1
ltguardare_2gt (look) Usyn BC
Number 105 Template_Type
Perception Template_Supertype
Psychological_event Domain
General Semantic Class
Perception Gloss
//free//
osservare con attenzione Event type
process Pred _Rep. Lex_Pred
(ltarg0gt,ltarg1gt) Derivation ltDerivational
relationgt Selectional Restr. arg0
Animate //concept// arg1default Entity
Formal isa
(1,ltSemUgtPerceptiongt)
ltpercepiregtPsych_ev Agentive
ltNilgt Constitutive instrument (1,
ltSemUgtBody_part) ltocchiogt
intentionality yes,no
//optional// yes Telic
ltNilgt Collocates Collocates
(ltSemU1gt,...ltSemUngt) Complex ltNilgt
Template for Perception
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Semantic Relations
Modular Representation of a SemU
Flexibility an extendable framework to allow
coherent future extensions tuning for specific
applications/text types

Pred. Layer
Predicate, arguments, selection restrictions, ..
Rel. Layer
Relations betw. SemUs
Features
Qualia multiple meaning dimensions in a sense
Derivation cross-PoS relations
Polysemy regular polysemous classes
Collocation collocational information
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Semantic Relations
..
Activity
..
..
?100 Rels.

• The targets of relations identify
• prototypical semantic information associated with
  a SemU
• elements of dictionary definitions of SemUs
• typical corpus collocates of the SemU

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Semantic Relations
Ala (wing)
ltfabbricaregt make
Agentive
SemU 3232 Type Part Part of an airplane
ltvolaregt fly
Used_for
Is_a_part_of
ltaeroplanogt airplane
Isa
SemU 3268 Type Part Part of a building
ltpartegt part
Isa
Used_for
Isa
SemU D358 Type Body_part Organ of birds for
flying
ltedificiogt building
Is_a_part_of
Is_a_part_of
SemU 3467 Type Role Role in football
ltuccellogt bird
ltgiocatoregt player
Isa
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Relations and Predicates
Pred_SELL ltARG0gt, ltARG1gt, ltARG2gt, ltARG3gt
SemU Sell V
Is_the_agent_of
SemU Sale N
SemU Seller N
Event_noun
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Argument Structure
Comprendere V
Comprensione N
SemU 61725 Type Cognitive_event To understand
SemU 61726 Type Cognitive_event Understanding
master
SemU 6962 Type Constitutive_state To include
verb_nominalization
Comprendere1 ltArg1 Humangt, ltArg2 Semioticgt
Comprendere2 ltArg1 Groupgt, ltArg2gt
master
problems with selection restrictions !!!
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SIMPLE/CLIPS figures (now)
(?11,000 Lex. Units) 16,903 SemUs

• Nouns 12161
• Verbs 3476
• Adjectives 1266
• Predicates 4368
• Templates
• Instrument 734
• Human 712
• PsychologicalProperty 586
• Profession 541
• Purpose_Act 535
• Part 503
• Human_Group 502
• Relational_Act 521
• AgentTemporaryActivity 320
• Domain 303

• Features Relations
• Agentive 1945
• EventTypeProcess 1846
• EventTypeTransition 1463
• AgentiveCause 1175
• Usedfor 1488
• Synonym 1258
• ResultingState 1197
• Isapartof 909
• Hasaspart 800
• Istheactivityof 611
• Objectoftheactivity 598
• AntonymGrad 575
• Createdby 525
• Agentverb 454
• Concerns 421

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Core Lexicons enlarged in National Projects

• PAROLE/SIMPLE/EWN start providing the common
  platform
• For the subsidiarity concept the process started
  at the EU level is continued at the national
  level
• extended in (at least) 9 National Projects
• (Danish, Greek, Italian, Portuguese, Swedish,
  ...)
• (to be) used in applications
• True Infrastructure of harmonised LRs in EU
• Basis for Multilingual LR
• ENABLER (coord. A. Zampolli)

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HarmonisationNeed for a Global View

• Interaction/sharing of data software/tools
• Need of compatibility among various components
• An exemplary cycle
• Formalisms
• Grammars
• Software Taggers,
• Chunkers, Parsers
• Representation
  Annotation
• Lexicon
  
  Corpora
• Software
• Acquisition Systems
• I/O Interfaces

Languages
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SIMPLE wrt EAGLES/ISLEStandards for
Multilingual Lexical resources
EAGLES guidelines for syntactic and semantic
lexicons
PAROLE/SIMPLE Lexicons
MT systems
ISLE recommendations for multilingual lexicons
Multilingual Lexicons
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Mission(http//lingue.ilc.pi.cnr.it/EAGLES96/isle
/ISLE_Home_Page.htm)

• MT and multilingual HLT need to enhance
  production, maintenance extension of
  computational lexical resources
• ISLE goals
• provide a common environment for the development,
  integration, interchange sharing of lexical
  resources with various types of linguistic
  information
• establish a virtuous circle betw. research,
  applications, standardization process lay down
  a bridge betw. the worlds of research and
  application
• mark the boundary between well-consolidated
  practice and theoretical achievements in
  multilingual HLT, and areas still open to
  research but critical for future technological
  improvements
• Crucial role of intercontinental cooperation for
  preparing ISLE recommendations and for their
  validation

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ISLE and MT

• Academic and industrial members of the MT
  community actively involved in the ISLE group
• Microsoft, NMSU, Sail Labs, Systran, UMIACS,
  UPenn, ISI, etc.
• Survey phase
• a number of lexical resources for MT systems
  surveyed by ISLE
• MT systems requirements provide the main
  reference points for ISLE work, to determine
• types of lexical information critical to SL ? TL
  mapping
• criteria to create bilingual resources from
  existing monolingual ones
• common data structures to develop reusable
  multilingual resources
• critical areas of the lexicon MWEs, complex
  transfer cases, collocational/example-based
  information, etc.

MWE parenthesis
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MWE in ISLE XMELLT - 2 types of MWE

• (Deverbal) nominalisations support (light) verbs
• make an acquisition1 (noun.act
  verb.possession)
• complete an acquisition1
• undertake an acquisition1
• make an application1 (noun/verb.communication)
• have an application1 in
• decide on an application1 (consider, hear)
• get an application1 (receive, take)
• submit an application1 (file)
• Noun(/Adj/Poss)Noun MW (Ital.
  NPP/NAdj/NVinf/...)
• air pollution
• job application
• murder suspect
• police action police scandal
• coltello da macellaio butcher's
  knife
• carta di credito credit card

1st
2nd
No equivalent structures
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The BoundariesSupport Verbs more than Light
Verbs? Nominalisations . to a broader set
1st

• Both verbs, combined with an event noun, whose
  subjects are
• participants in the event identified by the noun
• related to some scenario associated with the
  event
• Type 1 take an exam, give an exam
• Type 2 pass an exam, fail an exam, grade
  (evaluate) an exam
• Type 1 perform an operation, undergo an
  operation
• Type 2 survive an operation
• But also enlarge the concept of nominalisation
  to
• event/result/abstract nouns not morphologically
  derived
• dare un ceffone (to slap)
• provare rancore (to bear sb. a grudge)
• fare una festa (to have a party)
• fare festa (to have a holiday)
• fare festa a qno (to give sb. a warm welcome)
• prestare attenzione (to pay attention)

No verb (for diachronic reason)
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Hypothesis for encodingMelcuk type Lexical
Functions (LF)
1st

• to record semantic contribution and/or aspectual
  properties conveyed by the V
• to express argument-sharing betw 2 arg structures
  
• Oper1 perform an operation made an apology
• Oper2 undergo an operation merits discussion
  had a visit
• Func0 silence reign
• Laborij take into consideration
• Incep start the attack
• Cont maintain influence
• Fin complete the acquisition
• Liqu eradicate the disease
• Real keep the promise, approve the application
• AntiReal turn down, withdraw the application
• .

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Nominalisations examples from Corpus
1st

• accusa
• (supp-v formulare, lanciare, muovere,
  rivolgere,... (Oper1)
• subiredefault, beccarsi,
  attirarsi, rischiare,... (Oper2)
• mettere, porre,... sotto a.
  (Laborij)
• rintuzzare, rigettare, smontare,
  (Liqu)
• Problematic?
• ritorcere, rovesciare (...)
• sostenere, (...)
• ripetere, (...)
• ..
• __________________________________________________
  __________
• acquisizione
• (supp-v (fare)default, condurre,
  curare,effettuare,... (Oper1)
• varare,... (Incep)
• perfezionare, completare,
  concludere, (Fin)
• evitare, compromettere, (Liqu)
• sfumare, (LiquFunc0)
• Problematic?
• annuciare, dichiarare, (say)

Automatic acquisition
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Support Verbs what to list for multilingual
lexicons?
1st

• Decide if to include/list, for a noun
• all the verbs usable for a Melcukian LF
• INCEP cominciare default vs. varare,
  intraprendere,
• INCEP begin default vs. open (an
  investigation),
• OPER1say a prayer (not make, like with other
  speech act nouns)
• OPER1pay attention
• only those lexically dedicated to that noun
  (needed for generation) (not the general
  available by default for a LF)
• begin an exam/operation or finish an
  exam/operation
• similar words preferentially select different
  verbs to express similar meanings (same lexical
  functions) lexical preference

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Complex nominals in a multilingual framework
2nd

• Different syntactic patterns in L1 L2
• NNh ( head noun) in English is usually NhPP in
  Italian
• tooth brush spazzolino da denti
• the syntactic pattern is not predictable
• hair/clothes brush spazzola per capelli/abiti
• nail brush spazzola per le unghie
• travel agency agenzia di viaggi
• real estate agency agenzia immobiliare
• marriage bureau agenzia matrimoniale
• A MWE in L1 corresponding to a fully
  compositional phrase
• cucchiaino da caffè coffee spoon???
• For MT implies some conceptual (interlingual?)
  representation
• but the encoding process must find an
  appropriate MWE if it is called for
• analogous to blocking/pre-emption a
  regular/compositional process is not carried out
  (dispreferred) because the semantic space
  occupied by the concept associated with that
  formation is already claimed by some ready-made
  expression?

Fillmore
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Broader scope extension to non MWE?
2nd

• If look at devices in grammar that allow to
  produce new MWEs
• a continuum
• NPPgtcollocationgtmulti-wordgtidiom
• productive mechanisms in the language
• but idiosyncratic
• information at the borderline betw. grammar
  lexicon
• Amounts to
• describe productive modification relation of N in
  general
• in particular those lexically selected/preferred
  by a N (its semantic paradigm)
• MWE are a subset of these
• (give good hints to discover most prominent
  relations??)
• look at the semantic structure of Nouns i.e. at
  the variety of modifiers they can select by
  virtue of their meaning

Fillmore
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Noun Compounds/Complex Nominalsare pervasive
2nd

• There is a motivation in most NN construction
• the context provides it
• The FrameNet (SIMPLE) way
• appeal to specific frame structures (qualia
  structures) associated with the head noun,
• determine from corpus attestations which frame
  elements (qualia) can get instantiated as a
  modifier word
• container complex nominals can specify
• material (aluminium c., glass c., )
• contents (food c., trash c., )
• size (3 quart c., )
• function (shipping c., storage c., )
• ...

Fillmore Busa
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Noun Compounds/Complex Nominals
multidimensional semantic approaches
2nd

• a. FrameNet
• Container Frame Frame Elements
  Material,Contents,Size,Function
• Material aluminum container, glass c., metal c.,
  tin c.
• Contents food container, beverage c., trash c.,
  water c., milk c., fuel c.
• Size 3 quart container
• Function shipping container, storage c.
• b. SIMPLE
• Qualia Relations of "container" used in
  compounds
• Constitutive made_of MATERIAL
• aluminum container, glass c., metal c., tin
  c.
• Telic contains ENTITY
• food container, beverage c., trash c., water
  c., milk c., fuel c.
• Constitutivesize QUANTITY
• 3 quart container
• Telicis_used_for EVENT
• shipping container, storage c.

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Complex Nominals/Lexical Constructions in a
multilingual context
2nd

• describe vs. list?
• if a compound noun is clearly lexicalized, it's
  simply one of the words in L1
• but if it is an instance of some productive
  word-formation rule, we should describe it
• both describe list
• list explicitly in the lexical entry
• what is idiomatic/idiosyncratic wrt generation
  for
• lexical selection
• mucca pazza vs. matta
• prestare attenzione vs. pay attention
• structural pattern
• travel agency agenzia di viaggi
• marriage bureau agenzia matrimoniale (di
  matrimonio)
• real estate agency agenzia immobiliare

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In a multilingual context
2nd

• ...regularities in each language, but they dont
  match
• Both for decoding encoding, we need both
• a linguistic apparatus for interpretation
• (e.g. to go to a language where it is not a
  MWE
• cucchiaino da caffè for a
  Japanese useful to know used for)
• lists for idioms, for unpredictable/idiosyncrati
  c
• Same apparatus to interpret both MWE regular N
  constructions (similar power of expressiveness)
  general principles of semantic constitution of
  lex. items their combinatorics in terms e.g.
  of frames/qualia/
• basic sem. notions
• a general schema to characterise the problem,
  e.g.
• frame (qualia) structure of the headN
• semantic Type of the modifier N
• allow the headN to impose its interpretation on
  the modification rel.
• ...

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Complex nominals, e.g. knife (coltello) triggers
2nd

• a cutting frame (FrameNet)
• specific SIMPLE dimensions of meaning
• extensively evaluate whether qualia roles
  (already) encoded in SIMPLE correspond to what is
  necessary to interpret N-N modification
  relations
• SIMPLE Extended Qualia structure
• for the interpretation of the semantic relation
  betw. Ns
• (internal relational structure of MWE)
• butchers knife (coltello da macellaio) ? TELIC
  (used_by) Y Human ? PPda
• plastic knife (coltello di plastica) ?
  CONST (made_of) X Material ?PPdi
• table knife (coltello da tavola) ? TELIC
  (used_in) Z Location ?PPda
• hunting knife (coltello da caccia) ? TELIC
  (used_in_activity) E Activity ?Ppda
• piatto di legno ? CONST (made_of) X Material
  ?PPdi
• piatto di pasta ? CONST (contains) X Food ?PPdi

PP disambig.
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In SIMPLE possible extension
2nd

• Deverbal nominalisation
• noun murder (uccisione, delitto, omicidio
  (different sem. pref.)
• ?PPdi
  PREDMURDER(uccidere)
• ?PPda_parte_di, di
  ARG1agentHum/Anim?
• verb murder (uccidere)
  ARG2patientHum/Anim?
• ? subjNP
  MOD1instrWeapon
• ? objNP
  MOD2meansAction
• MOD3......
• instr PPcon Weapon (knife m., con coltello)
• means PPper Action (strangulation m., per
  strangolamento)
• loc Ppplocdi Location (Kent State murders,
  nel ...)
• time Ppptimedi Time (1983 murders, del 1983)

As if it were a Situation
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Monolingual Linguistic Representation
Strategy

• consider as the starting point for MILE the
  edited union of the basic notions represented in
  the existing syntactic/semantic lexicons (their
  models)
• evaluate their notions wrt EAGLES recommendations
  for syntax and semantics
• evaluate their usefulness adequacy for
  multilingual tasks
• evaluate integrability of their notions in a
  unitary MILE
• look for deficient areas, e.g. MWE
• ...

To be decided should ISLE reach a consensus at
the level of the types of information only, or
also at the level of their token values? .
different answers for diff. notions
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the Multilingual ISLE Lexical Entry
(MILE)

• General methodological principles (from EAGLES)
• Basic requirements for the MILE
• Discover and list the (maximal) set of basic
  notions needed to describe the MILE (up to which
  level standardisation is feasible?)
• Granularity
• The leading principle for the design of the MILE
  the edited union of existing lexicons/models
  (redundancy is not a problem)
• Modular and layered various degrees of
  specification possible
• Allow for underspecification ( hierarchical
  structure)

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The MILE

• Main features
• factor out primitive units of lexical information
• explicit representation of information to be
  targeted by multilingual NLP tools
• rely on lexical analyses with the highest degree
  of inter-theoretical agreement
• avoid framework-specific representational
  solutions
• open to different paradigms of multilinguality
• oriented to the creation of large-scale lexical
  databases

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MILE

• Objective definition of the MILE
• as a meta-entry to act as a common format for
  resource sharing and integration/architecture for
  lexical data encoding
• ? its basic notions
• ? general architecture
• formalized as an entity-rel.
• model (XML, RDF, etc.)
• with a tool to support it
• open to task- system-dependent parameterisation

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Agreed Principles

• MILE builds on the monolingual entry expands it
• MILE incorporates previous EAGLES recommendations
  
• is the complete entry
• adopt as starting point the PAROLE/SIMPLE DTD
• to be revised, augmented, ...
• We consider 2 broad categories of
  applications
• MT
• CLIR (linking module may be simpler/ontology
  based)
• (label info types wrt application)

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Modularity in MILE

• Advantages
• Flexibility of representation
• Easy to customise and update
• Easy integration of existing resources
• High versatility towards different applications
• Modularity at least under three respects
• in the macrostructure and general architecture of
  the MILE
• in the microstructure of the MILE
• monolingual linguistic representation (previous
  EAGLES revised/updated)
• collocational/corpus-driven information (new)
• multilingual apparatus (e.g. transfer conditions
  and actions interlingua) (new)
• in the specific microstructure of the MILE
  word-sense

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Modularity in MILE
A. MILE Macrostructure
C. Word-Sense Microstructure
MILE
B. MILE Microstructure
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The MILE Architecture Monolingual Lexical
Description

• three independent and yet linked layers
  characterising the MILE in a source language
• possibly corresponds to the typology of
  information contained in major existing lexicons,
  such as PAROLE-SIMPLE, (Euro)WordNet, COMLEX,
  FrameNet, etc.
• simple and complex lexical unit (to account for
  MWEs)
• various degrees of granularity of lexical units
  representation

semantic layer
correspondence conditions
syntactic layer
morphological layer
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The MILE ArchitectureMultilingual Layer

• acts as an (independent) interface layer between
  monolingual lexicons

multilingual layer
semantic layer
correspondence conditions
syntactic layer
Lexicon 1
Lexicon 2
morphological layer
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The MILE Multilingual Layer.(NEW)

• Correspondences can be established between
  different types of linguistic objects (strings,
  syntactic descriptions, semantic elements,
  predicates, etc.)
• Transfer tests and actions to target various
  types of lexical information in the monolingual
  layers
• constrain syntactic positions and their fillers
• lexicalize syntactic positions
• add positions or arguments
• add new features to define more fine-grained
  sense distinctions relevant at the multilingual
  level
• restructuring argument configurations
• collocational information
• ...

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Paths to Discover theBasic Notions of MILE

• clues in dictionaries to decide on target
  equivalent
• guidelines for lexicographers
• clues (to disambiguate/translate) in corpus
  concordances
• lexical requirements from various types of
  transfer conditions and actions in MT systems
• lexical requirements from interlingua-based
  systems

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• Organisational Proposal
• division of labour

• Highlighted some hot issues assigned tasks
• sense indicators (EU)
• selection preferences (EU)
• lexicographic relevance (EU)
• argument structure (US)
• MWE (EU US)
• collocations parallel corpora (US)
• modifiers (EU)
• semantic relations (EU)
• transfer conditions (EU US)
• collocational patterns (US)
• ontology (US)
• metaphors (EU)
• interlingua requirements (US)
• spoken lexicon (EU)
• meta-representation (US EU)
• ...

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Organisational ProposalThe tasks will lead to

• an in-depth analysis of each area aiming at
  identifying
• the most stable solutions adopted in the
  community
• linguistic specifications and criteria
• possible representational solutions, their
  compatibility, etc.
• evaluation of their respective weight/importance
  in a multilingual lexicon (towards a layered
  approach to recommendations)
• open issues and current boundaries of the
  state-of-the-art (which cannot be standardised
  yet)
• model limitations through creation of a sample
  dictionary
• see how the various pieces fit together can be
  merged in a unified proposal
• evaluate if we can combine in a hybrid
  super-model the transfer interlingua approaches

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Information Types examples
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CLWG Ongoing Activities

• to prepare a preliminary proposal of the MILE
• existing models for lexical representation and
  data interchange (Genelex, Olif, etc.) are
  explored
• model limitations and expressive power are tested
  through creation of sample entries in a few
  languages
• groups at work
• lexical description and information types of
  relevant info
• lexicographic exploration systematic summary
  classification of types of transfer tests (also
  extracted from MRDs)
• multilingual correspondences
• lexical data modeling format representation
  issues
• tool development

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Representation issues

• Working with GENELEX, lexicon development work is
  (can be) affected by
• impossibility (or difficulty) of defining
  abstract and general classes or types of objects
• lack of inheritance mechanisms
• lack of default expression and default rewriting
  mechanisms
• Cf. Lexical templates in SIMPLE
• not included in the GENELEX data-structure
• implemented in the editing sw. tool
• very useful to capture relevant lexical
  generalizations, enhance consistency in encoding,
  speed-up lexicographers work, etc.

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CLWG Ongoing Activity
MILE Lexical Objects Formal Specifications
MILE Lexical Entry Formal Specifications
MILE Shared Lexical Objects
User Defined Lexical Objects
Monolingual Multilingual Lexicons
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• MILE Repository of Shared Lexical Objects
• Basic syntactic constructions (e.g. transitive,
  etc.)
• (Micro-)semantic objects (e.g. features,
  relations)
• (Macro-)semantic objects (e.g. lexical templates)
• Multilingual constructions (e.g. basic transfer
  conditions and actions)

MILE Shared Lexical Objects
Simplify using MILE

• - New Lexical objects defined by the User
  according to the common MILE formal
  data-structure specification.
• Sub-types of the Shared MILE Objects
• Possibly enriched with metadata defining their
  semantics and usage

User-Defined Lexical Objects
Monolingual Multilingual Lexicons
- Lexical entries obtained by referring to
various lexical objects (both Shared and
User-defined) - The MILE lexical entry model
specifies how lexical objects can be combined to
achieve the proper lexical representation
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Involvement of Asian Languages

• participation in last meetings
• some input from Asia
• formal cooperation EU-ASIA steps to put in
  motion

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Impact synergies

• real impact to be evaluated later
• through the use in applications
• already its being a US/EU project
• the Asian interest
• synergies now, e.g.
• PAROLE/SIMPLE (also instantiated in 9 national
  projects) main input
• EuroWordNet provides input
• XMELLT (NSF) provides input
• OLIF expects ( provides) input
• SALT complementary
• ENABLER validation ( expects input)
• ELSNET validation
• SENSEVAL validation
• NIMM WG for Metadata for CL(also with the US
  OLAC)
• ...

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Target . Multilingual Content
Management the
Resources viewpoint

• The relevance/impact of (good vs. less good) LRs
  for high-quality Cross/Multilingual systems is
  high, even if not easily measurable.
• Different applications, component technologies -
  approaches within - need different info types
  (e.g. CLIR or content access systems wrt MT)
• For each, need to specify (not an easy task)
• clear lexical/linguistic/conceptual requirements
• priority info types (which, how encoded, etc.)
• the respective role of e.g. annotated corpora,
  mono- bi- multilingual lexicons (with different
  info types), ontologies, KBs

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Economic Feasibilityfor which (Multilingual)
Resources to invest?

• Wrt short- vs. medium-term impact
• Basic, general purpose bi-/multilingual lexicons,
  but to be tuned, adapted to different
  applications
• need of robust systems able to acquire/tune
• (multilingual) lexical/linguistic/conceptual
  knowledge,
• to accompany static basic resources
• We shouldnt rely only on parallel
  corpora. More advisable to aim at
• reliable methods for acquisition
  use of comparable corpora,
• accompanied by
• robust technologies for annotation
  (at different levels morphosyntactic,
• syntactic/functional, semantic,
  ), and by
• a shared set of (text) annotation schemata

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Target.. Multilingual
Knowledge Management
Technical Feasibility

• Prerequisite is it an achievable goal a commonly
  agreed text/lexicon annotation protocol also for
  the semantic/conceptual level (to be able to
  automatically establish links among different
  languages)?
• Yes, at the lexical level
• More complex, for corpus annotation?

EAGLES/ISLE
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Content for practical use Gap betw. Resources
and Systems?
?

• If we had real-size lexicons with very
  fine-grained semantic/conceptual info, would
  there be systems (non ad-hoc toy systems) able to
  use them?
• A vicious circle between
• i) lack of suitable, large-size and knowledge
  intensive, resources (lexicons and corpora, with
  many different types of syntactic and semantic
  information encoded), and
• ii) systems ability to use them effectively
• The two targets should be pursued in parallel,
• should closely interact with each other, and
• be gradually integrated