Diapositiva 1

1
Infrastructural Language Resources Standards
for Multilingual Computational Lexicons Nicoletta
Calzolari with many others Istituto di
Linguistica Computazionale - CNR -
Pisa glottolo_at_ilc.cnr.it
2
The ENABLER Mission

• Language Resources (LRs) Evaluation central
  component of the linguistic infrastructure
• LRs supported by national funding in National
  Projects
• Availability of LRs also a sensitive issue,
  touching the sphere of linguistic and cultural
  identity, but also with economical and political
  implications
• The ENABLER Network of National initiatives,
  aims at enabling the realisation of a
  cooperative framework
• formulate a common agenda of medium- long-term
  research priorities
• contribute to the definition of an overall
  framework for the provision of LRs

3
towards .

• Only
• Combining the strengths of different initiatives
  communities
• Exploiting at best the modus operandi of the
  national funding authorities in different
  national situations
• Responding to/anticipating needs and priorities
  of RD industrial communities
• Promoting the adoption of de facto standards,
  best practices
• With a clear distinction of tasks roles for
  different actors
• We can produce the
• synergies, economy of scale, convergence
  critical mass
• necessary to provide the infrastructural LRs
  needed to realise the full potential of a
  multilingual global information society

4
Lexicon and Corpusa multi-faceted interaction

• L ? C tagging
• C ? L frequencies (of different linguistic
  objects)
• C ? L proper nouns, acronyms,
• L ? C parsing, chunking,
• C ? L training of parsers
• C ? L lexicon updating
• C ? L collocational data (MWE, idioms, gram.
  patterns ...)
• C ? L nuances of meanings semantic clustering
• C ? L acquisition of lexical (syntactic/semantic)
  knowledge
• L ? C semantic tagging/word-sense disambiguation
• ?
  (e.g. in Senseval)
• C ? L more semantic information on LE
• C ? L corpus based computational lexicography
• C ? L validation of lexical models
• C ? L
• L ? C ...

5
...Language as a Continuum
Lexicon Corpus as two viewpoints on the same
ling. object . even more in a multilingual
context

• Interesting - and intriguing - aspects of corpus
  use
• impossibility of descriptions based on a
  clear-cut boundary betw. what is admitted and
  what is not
• in actual usage, language displays a large number
  of properties behaving as a continuum, and not as
  properties of yes/no type
• the same is true for the so-called rules, where
  we find more a tendency towards rules than
  precise rules in corpus evidence
• difficult to constrain word meaning within a
  rigorously defined organisation by its very
  nature it tends to evade any strict boundary
• 
  BUT

6
Extraction from texts vs.formal representation
in lexicons

• It is difficult to constrain word meaning within
  a rigorously defined organisation by its very
  nature it tends to evade any strict boundary
• The rigour and lack of flexibility of formal
  representation languages causes difficulties when
  mapping into it NL word meaning, ambiguous and
  flexible by its own nature
• No clear-cut boundary when analysing many
  phenomena its more a continuum
• The same impression if one looks at examples of
  types of alternations
• no clear-cut classes across languages
• or within one language

7
Correlation between different levels of
linguistic description in the design of a
lexical entry

• To understand word-meaning
• Focus on the correlation between syntactic and
  semantic aspects
• But other linguistic levels - such as morphology,
  morphosyntax, lexical cooccurrence, collocational
  data, etc. - are closely interrelated/involved
• These relations must be captured when accounting
  for meaning discrimination
• The complexity of these interrelationships makes
  semantic disambiguation such a hard task in NLP
• Textual corpora as a device to discover and
  reveal the intricacy of these relationships
• Frame/SIMPLE semantics as a device to unravel and
  disentangle the complex situation into elementary
  and computationally manageable pieces

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towards Corpus based Semantic Lexicons at least
in principle

• both in the design of the model ,
• in the building of the lexicon (at least
  partially)
• with (semi-)automatic means
• Design of the lexical entry with a combined
  approach
• theoretical e.g. Fillmore Frame Semantics/
• Pustejovsky Generative Lexicon,
• empirical Corpus evidence
• even if not always there are sound and explicit
  criteria for classification according to frame
  elements/qualia relations/...

9
Infrastructure of Language Resources...
...static

• Semantic networks Euro-/ItalWordNet
• Lexicons PAROLE/SIMPLE/CLIPS
• TreeBanks

International Standards
But they will never be complete
dynamic

• Lexical acquisition systems (syntactic
  semantic) from corpora
• Infrastructure of tools
• Robust morphosyntactic syntactic analysers
• Word-sense disambiguation systems
• Sense classifiers
• ...

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ItalWordNet Semantic Network Italian module of
EuroWordNet

• 50.000 lemmas organized in synonym groups
  (synsets), structured in hierarchies linked by
  130.000 semantic relations
• 50.000 hyperonymy/hyponymy relations
• 16.000 relations among different POS (role,
  cause, derivation, etc..)
• 2.000 part-whole relations
• 1.500 antonymy relations, etc.
• Synsets linked to the InterLingual Index
  (ILIPrinceton WordNet),
• Through the ILI link to all the European WordNets
  (de-facto standard)
• to the common Top Ontology
• Possibility of plug-in with domain terminological
  lexicons
• (legal, maritime)
• Usable in IR, CLIR, IE, QA, ...

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EuroWordNet Multilingual Data Structure
12
TOP ConceptsObject,Artifact,Building
Hyperonym edificio,..
home, domicile, .. house
Casa, abitazione, dimora
Role_location stare, abitare, ...
Hyponym villetta catapecchia, bicocca, ..
cottage bungalow
Role_target_direction rincasare
Role_patient affitto, locazione
Mero_part vestibolo
stanza
Synsets linked by Semantic Relations in
ItalWordNet
Holo_part casale frazione
caseggiato
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Jur-WordNet

• With ITTG-CNR (Istituto di Teoria e Tecniche
  dellinformazione Giuridica)
• Jur-WordNet ð Extension for the juridical domain
  of ItalWordNet
• Knowledge base for multilingual access to sources
  of legal information
• Source of metadata for semantic mark-up of legal
  texts
• To be used, together with the generic
  ItalWordNet, in applications of Information
  Extraction, Question Answering, Automatic
  Tagging, Knowledge Sharing, Norm Comparison, etc.

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Terminological Lexicon of Navigation Sea
Transportation
ð Nolo
Synsets ð 1.614 Lemmas ð 2.116 Senses ð
2.232 Nouns ð 1.621 Verbs ð 205 Adjectives ð
35 Proper Nouns ð 236
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PAROLE/SIMPLE 12 harmonised computational lexicons
http//www.ilc.cnr.it/clips/
SIMPLE Ital. Sem. Lex. 98-2000
PAROLE Ital. Synt. Lex. 96-98
SGML
SGML
semantics 10,000 senses
morphology 20,000 entriessyntax
20,000 words
CLIPS 2000-2004
phonologymorphology 55,000 words syntax
semantics 55,000 senses
XML
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machine language learning
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machine language learning
linguistic learning
development of conceptual networks
linguistic change models
language usage models
adaptive classification systems
information extraction
bootstrapping of lexical information
bootstrapping of grammars
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Architecture for linguistic knowledge acquisition
...
terminology
unstructured text data
annotation tools
LKG
cross-lingual information retrieval
annotated data
lexica
lexica
multi-lingual information extraction
machine learning for linguistic knowledge
acquisition
lexicon model
user needs
multi-lingual text mining
. towards dynamic lexicons, able to auto-enrich
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HarmonisationMore more Need of a Global
Viewfor Global Interoperability

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

Languages
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A short guide to ISLE/EAGLES http//www.ilc.cnr
.it/EAGLES96/isle/ISLE_Home_Page.htm

• Multilingual Computational Lexicon
• Working Group

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Target the Multilingual ISLE Lexical
Entry (MILE)

• General methodological principles (from EAGLES)
• high granularity factor out the (maximal) set of
  primitive units of lexical info (basic notions)
  with the highest degree of inter-theoretical
  agreement
• modular and layered various degrees of
  specification possible
• explicit representation of info
• allow for underspecification ( hierarchical
  structure)
• leading principle edited union of existing
  lexicons/models (redundancy is not a problem)
• open to different paradigms of multilinguality
• oriented to the creation of large-scale
  distributed lexicons

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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 actions in MT systems
• lexical requirements from interlingua-based
  systems

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Designing MILESteps towards MILE

• Creating entries (Bertagna, Reeves, Bouillon)
• Identifying the MILE Basic Notions
  (Bertagna,Monachini,Atkins,Bouillon)
• Defining the MILE Lexical Model (Lenci,
  Calzolari, etc.)
• Formalising MILE (Ide)
• Development of the ISLE Lexical Tool (Bel)
• ISLE spoken language multimodality (Gibbon)
• Metadata for the lexicon (Peters, Wittenburg)
• A case-study MWEs in MILE (Quochi, lenci,
  Calzolari)

• the MILE Basic Notions
• the MILE Lexical Model

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The MILE Basic Notions (the EAGLES/ISLE CLWG)

• Basic lexical dimensions info-types relevant to
  establish multilingual links
• Typology of lexical multilingual correspondences
  (relevant conditions actions)
• Identified by
• creating sample multilingual lexical entries
  (Bertagna, Reeves)
• investigating the use of sense indicators in
  traditional bilingual dictionaries (Atkins,
  Bouillon)
• .

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The MILE Lexical Classes Data Categories for
Content Interoperability

• Francesca Bertagna, Alessandro Lenci, Monica
  Monachini, Nicoletta Calzolari
• ILCCNR Pisa
• Pisa University

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Overview

• MILE Lexical Model with Lexical Objects and Data
  Categories
• Mapping of existing lexicons onto MILE
• RDF schema and DC Registry for some
  pre-instantiated lexical objects together with a
  sample entry from the PAROLE-SIMPLE lexicons in
  MILE
• Future

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The MILE Lexical Model
Guidelines syntactic semantic lexicons
GENELEX Model
PAROLE-SIMPLE Lexicons
Multilingual Lexicons (EuroWordNet, etc.)
where after?
MILE Lexical Model
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The MILE Main Features

• A general architecture devised as a common
  representational layer for multilingual
  Computational Lexicons
• both for hand-coded and corpus-driven lexical
  data
• Key features
• Modularity
• Granularity
• Extensibility and openess - User-adaptability
• Resource Sharing
• Content Interoperability
• Reusability

Semantic Web technologies standards applied at
Lexicon modelling
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The MILE Lexical Model (MLM)

• The MLM core is the Multilingual ISLE Lexical
  Entry (MILE)
• a general schema for multilingual lexical
  resources
• a lexical meta-entry as a common representational
  layer for multilingual lexicons
• Computational lexicons can be viewed as different
  instances of the MILE schema

MILE Lexical Model
lexicon2
lexicon1
lexicon3
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MILEthe building-block model

• The MILE architecture is designed according to
  the building-block model
• Lexical entries are obtained by combining various
  types of lexical objects (atomic and complex)
• Users design their lexicon by
• selecting and/or specifying the relevant lexical
  objects
• combine the lexical objects into lexical entries
• Lexical objects may be shared
• within the same lexicon (intra-lexicon
  reusability)
• among different lexicons (inter-lexicon
  reusability)

31
MILEthe building-block model
32
Modularity in MILE
Horizontal organization, where independent, but
interlinked, modules allow to express different
dimensions of lexical entries
multi-MILE
multilingual correspondence conditions
multiple levels of modularity
33
The Mono-MILE

• Each monolingual layer within Mono-MILE
  identifies a basic unit of lexical description

SemU
basic unit to describe the semantic properties of
the MU
semantic layer
basic unit to describe the syntactic behaviour of
the MU
SynU
syntactic layer
basic unit to describe the inflectional and
derivational morphological properties of the word
MU
morphological layer
34
The Mono-MILE
MU
Within each layer, a basic linguistic information
unit is identified
35
Granularity in MILE

• Concerns the vertical dimension. Within a given
  lexical layer, varying degrees of depth of
  lexical descriptions are allowed, both shallow
  and deep lexical representations

36
Defining the MLM

• The MLM is designed as an E-R model (MILE Entry
  Schema)
• defines the lexical objects and the ways they can
  be combined into a lexical entry
• The MLM includes 3 types of lexical objects
• MILE Lexical Classes (MLC)
• MILE Lexical Data Categories (MDC)
• MILE Lexical Operations (MLO)

37
The MILE Lexical Objects

• Within each layer, basic lexical notions are
  represented by lexical objects
• MILE Lexical Classes MLC
• MILE Data Categories MDC
• Lexical operations
• They are an ontology of lexical objects as an
  abstraction over different lexical models and
  architectures

38
The MILE E/R diagrams

• The lexical objects are described with E-R
  diagrams which define them and the ways they can
  be combined into a lexical entry

39
MILE Lexical Objects Syntactic Layer
hasSyntacticFrame
MLCSyntacticFrame
1..
MLCSynU
hasFrameSet
MLCFrameSet

composedby
MLCComposition

correspondTo
MLCSemU

MLCCorrespSynUSemU
40
expanding one node.

SynU

SyntacticFrame
Construction
Self
Slot
Slot
Function
Phrase
41
MILE Lexical Objects Semantic Layer
belongsToSynset
MLCSynset

MLCSemU
hasSemFrame
MLCSemanticFrame
0..1
hasSemFeature
MLCSemanticFeature

hasCollocation
MLCCollocation

semanticRelation
MLCSemU

MLCSemanticRelation
42
MILE Lexical Objects Synt-Sem Linking
hasSourceSynu
MLCSynU
MLCCorrespSynUSemU
1
hasTargetSemu
MLCSemU
1
MLCPredicativeCorresp
hasPredicativeCorresp
1
MLCSlotArgCorresp
IncludesSlotArgCorresp
0..
43
Syntax-Semantics Linking
CorrespSynUSemU
PredCorresp
Slot0Arg1 Slot1Arg0
44
Syntax-Semantics Linking
John gave the book to Mary John gave Mary the book
SynU1
SemU1
obj_NP
obl_PP_to
subj_NP
Semantic_FrameGIVE
Arg2 Theme
Arg3 Goal
Arg1 Agent
SynU2
obj_NP
obj_NP
subj_NP
45
Syntax-Semantic Linking in SIMPLE
SynU_migliorare
Intransitive structure
Slot0 Ø
Transitive structure Slot0 Slot1
Frameset

CorrespSynUSemU
CorrespSynUSemU
isomorphic
non-isomorphic
SlotArgCorresp
SlotArgCorresp
PRED_ migliorare
ARG0Agent
ARG1Patient
SemU1_migliorare
SemU2_migliorare
CAUSE_CHANGE_OF_STATE
CHANGE_OF_STATE
46
The Multilingual layer
hasMUMUCorr
MUMUCorresp
1..0
MultiCorresp
hasSynUSynuCorr
SynUSynUCorresp
1..0
hasSemUSemUCorr
SemUSemUCorresp
1..0
hasSynsetMultCorr
SynsetMultCorresp
1..0
hasSemFrameCorr
SemanticFrameMultCorresp
1..0
47
MILE approach to multilinguality

• Open to various approaches
• transfer-based
• monolingual descriptions are used to state
  correspondences (tests and actions) between
  source and target entries
• interlingua-based
• monolingual entries linked to
  language-independent lexical objects (e.g.
  semantic frames, primitive predicates, etc.)

48
The Multi-MILE

• Multi-MILE specifies a formal environment to
  express multilingual correspondences between
  lexical items
• Source and target lexical entries can be linked
  by exploiting (possibly combined) aspects of
  their monolingual descriptions
• monolingual lexicons act as pivot lexical
  repositories, on top of which language-to-language
  multilingual modules can be defined

49
The Multi-MILE

• Multi-MILE may include
• Multlingual operations to establish transfer
  links between source and target mono-MILE
• Multlingual lexical objects
• enrich the source and target lexical
  descripotions, but
• do not belong to the monolingual lexicons
• Language-independent lexical objects
• Primitive semantic frames, interlingual
  synsets, etc.
• Relevant for interlingua approaches to
  multilinguality

50
Multi-MILE
IT_SemU_2 ? En_SemU_1 IT_SynU_2 ?
En_SynU_1 IT_Slot_0 ?EN_Slot_1 IT_Slot_1 ?
EN_Slot_0
AddFeature to source SemU HUMAN
AddSlot to target SynU MODIF PP_with
51
Multi-MILE
IT Lexicon
EN Lexicon
multilingual conditions
finger
modif(mano)
dito
modif(piede)
toe
multilingual conditions
entrare to enter
run PP_into
PP_di_corsa
52
MILE Lexical Classes

• Represent the main building blocks of lexical
  entries
• Formalize the MILE Basic Notions
• Define an ontology of lexical objects
• represent lexical notions such as semantic unit,
  syntactic feature, syntactic frame, semantic
  predicate, semantic relation, synset, etc.
• Similar to class definitions in OO languages
• specify the relevant attributes
• define the relations with other classes
• hierarchically structured

53
MILE Lexical Classesan ontology of lexical
objects
54
MILE Lexical Data Categories

• MDC are instances of the MILE lexical Classes
• Can be used off the shelf or as a departure
  point for the definition of new or modified
  categories
• Enable modular specification of lexical entities
  using all or parts of the lexical information in
  the repository
• Each MDC respresents a resource
• uniquely identified by a URI
• Two types of MDC
• Core MDC
• belong to shared repositories (Lexical Data
  Category Registry)
• lexical objects and linguistic notions with wide
  consensus
• User Defined MLDC
• user-specific or language specific lexical
  objects

55
The MILE Data Categories

• Instances of the MILE Lexical Classes are Data
  Categories
• MDC can belong to a shared repository or be
  user-defined

MLC
Core MDC
User-defined MDC
56
The MILE Data Categories User-adaptability and
extensibility
MLCSemanticFeature
instance_of
Core
HUMAN ARTIFACT EVENT ANIMAL GROUP
AGE MAMMAL
UserDefined
57
MILE Lexical Data Categories
MLMFeature
MLMGrammaticalFunction
58
MILE Lexical Operations

• They are used to state conditions and perform
  operations over lexical entries
• Link syntactic slots and semantic arguments
• Constrain the syntax-semantic link
• Express tests and actions in the transfer
  conditions in the multi-MILE
• They provide the glue to link various
  independent intra-lexical and inter-lexical
  components

59
Multilingual Operations

• Source-to-target language transfer conditions can
  be expressed by combining multilingual operations
• Three types of multingual operations
• Multilingual correspondences
• Link a source lexical object (MU, SemU, SynU,
  semantic argument, syntactic slot) and a target
  lexical object (MU, SemU, SynU, semantic
  argument, syntactic slot)
• Add-operations
• Add lexical information relevant for the
  cross-lingual link, but not present in the source
  or target mono-MILE
• Constrain-operations
• Constrain the transfer link to some portions of
  source and target mono-MILE

60
Defining the MLM
MILE Entry Schema
MILE Lexical Classes
RDF/S Descriptions
61
RDF Instantiation of the MLM
Lexicon2
Resources
Lexicon1
Lexicon3
Metadata
Lexical Objects
Resources
Lexical Classes
Lexical Data Categories
62
MILE Lexical Model

• Ideal structure for rendering in RDF
• hierarchy of lexical objects built up by
  combining atomic data categories via clearly
  defined relations
• Proof of concept
• Create an RDF schema for the MILE Lexical Model
• version 1.2
• Instantiate MILE Lexical Data Categories

63
User-Adaptability and Resource Sharing in MILE

• Compatible with different models of lexical
  analysis
• Relational semantic models (e.g. WordNet)
• Syntactic and semantic frames
• Ontology-based lexicons
• Compatible with different degrees of
  specification
• Deep lexical representations (e.g. PAROLE-SIMPLE)
• Terminological lexicons
• Compatible with different paradigm of
  multilinguality
• Lexicons for Transfer Based MT
• Interlingua-based lexicons

64
The MILE Lexical Model
MILE Lexical Model
65
RDF Instantiation of the MLM

• Enable universal access to sophisticated
  linguistic info
• Provide means for inferencing over lexical info
• Incorporate lexical information into the Semantic
  Web
• W3C standards
• Resource Definition Framework (RDF)
• Ontology Web Language (OWL)
• Built on the XML web infrastructure to enable the
  creation of a Semantic Web
• web objects are classified according to their
  properties
• semantics of relations (links) to other web
  objects precisely defined

66
The RDF Schema

• Defines classes of objects (MLC) and their
  relations to other objects
• Like a class definition in Java, etc.
• Classes and properties in the schema correspond
  to the E-R model
• Can specify sub-classes/sub-properties and
  inheritance

67
Goals

• Lexical information will form a central component
  of semantic information
• Need a standardized, machine processable format
  so that information can be used, merged with
  others
• Main task get the data model right

See Semantic Web
68
Advantages of RDF

• Modularity
• Can create instances of bits of lexical
  information for re-use in a single lexicon or
  across lexicons
• Instances can be stored in a central repository
  for use by others
• Can use partial information or all of it
• Building block approach to lexicon creation
• Web-compatible
• RDF instantiation will integrate into Semantic
  Web
• Inferencing capabilities

69
Example

• Three parts
• RDF Schema for lexical entries
• Defines classes and properties, sub-classes, etc.
• Sample repository of RDF-instantiated lexical
  objects
• Three levels of granularity
• Sample lexicon entries
• Use repository information at different levels

70
Sample Repositories

• repository of enumerated classes for lexical
  objects at the lowest level of granularity
• definition of sets of possible values for various
  lexical objects
• repository of phrases for common phrase types,
  e.g., NP, VP, etc.
• repository of constructions for common syntactic
  constructions

71
ltrdfsClass rdfabout"http//www.cs.vassar.edu/i
de/rdf/isle-enumerated-classesFunctionType"gt ltowl
oneOfgt ltrdfSeqgt ltrdfligtSubjlt/rdfligt
ltrdfligtObjlt/rdfligt
ltrdfligtComplt/rdfligt ltrdfligtArglt/rdfligt
ltrdfligtIobjlt/rdfligt
lt/rdfSeqgt lt/owloneOfgt lt/rdfsClassgt
ltrdfsClass rdfabout"http//www.cs.vassar.edu/
ide/rdf/isle-enumerated-classesSynFeatureName"gt
ltowloneOfgt ltrdfSeqgt
ltrdfligttenselt/rdfligt ltrdfligtgenderlt/rdf
ligt ltrdfligtcontrollt/rdfligt
ltrdfligtpersonlt/rdfligt ltrdfligtauxlt/rdfli
gt lt/rdfSeqgt lt/owloneOfgt lt/rdfsClassgt
ltrdfsClass rdfabout"http//www.cs.vassar.edu/
ide/rdf/isle-enumerated-classesSynFeatureValue"gt
ltowloneOfgt ltrdfSeqgt
ltrdfligthavelt/rdfligt ltrdfligtbelt/rdfligt
ltrdfligtsubject_controllt/rdfligt
ltrdfligtobject_controllt/rdfligt
ltrdfligtmasculinelt/rdfligt
ltrdfligtfemininelt/rdfligt lt/rdfSeqgt lt/owloneO
fgt lt/rdfsClassgt
Enumerated classes
72
Sample LDCR for a Phrase Object
ltrdfRDF xmlnsrdf"http//www.w3.org/1999/02/22-r
df-syntax-ns" xmlnsrdfs"http//www.w3.
org/2000/01/rdf-schema"
xmlnsmlc"http//www.cs.vassar.edu/ide/rdf/isle-
schema-v.6"gt ltPhrase rdfID"NP"
rdfslabel"NP"/gt ltPhrase rdfID"Vauxhave"gt
lthasSynFeaturegt ltSynFeaturegt
lthasSynFeatureName rdfvalue"aux"/gt
lthasSynFeatureValue rdfvalue"have"/gt
lt/SynFeaturegt lt/hasSynFeaturegt lt/Phrasegt
lt/rdfRDFgt
73
Sample LDCR entry for a Construction object
ltrdfRDF xmlnsrdf"http//www.w3.org/1999/02/22-r
df-syntax-ns" xmlnsrdfs"http//www.w3
.org/2000/01/rdf-schema"
xmlns"http//www.cs.vassar.edu/ide/rdf/isle-sche
ma-v.6"gt ltConstruction
rdfID"TransIntrans"gt ltslotgt
ltSlotRealization rdfID"NPsubj"gt
lthasFunction rdfvalue"Subj"/gt
ltfilledBy rdfresource
"http//www.cs.vassar.edu/ide/rdf/isle-datcats/Ph
rasesNP"/gt lt/SlotRealizationgt
lt/slotgt ltslotgt ltSlotRealization
rdfID"NPobj"gt lthasFunction
rdfvalue"Obj"/gt ltfilledBy
rdfresource "http//www.cs.vassar.edu
/ide/rdf/isle-datcats/PhrasesNP"/gt
lt/SlotRealizationgt lt/slotgt lt/Constructiongt lt/
rdfRDFgt
74
Full entry
ltEntry rdfID"eat1"gt lthasSynu
rdfparseType"Resource"gt ltSynU
rdfID"eat1-SynU"gt ltexamplegtJohn ate
the cakelt/examplegt lthasSyntacticFramegt
ltSyntacticFrame rdfID"eat1SynFrame"gt
lthasSelfgt ltSelf
rdfID"eat1Self"gt
ltheadedBygt ltPhrase
rdfID"Vauxhave"gt
lthasSynFeaturegt
ltSynFeaturegt
lthasSynFeatureName rdfvalue"aux"/gt
lthasSynFeatureValue
rdfvalue"have"/gt
lt/SynFeaturegt
lt/hasSynFeaturegt
lt/Phrasegt lt/headedBygt
lt/Selfgt
lt/hasSelfgt Continued
75
Continued from previous slide
lthasConstructiongt
ltConstruction rdfID"eat1Const"gt
ltslotgt
ltSlotRealization rdfID"NPsubj"gt
lthasFunction rdfvalue"Subj"/gt
ltfilledBy rdfvalue"NP"/gt

lt/SlotRealizationgt lt/slotgt
ltslotgt
ltSlotRealization rdfID"NPobj"gt
lthasFunction rdfvalue"Obj"/gt
ltfilledBy rdfvalue"NP"/gt
lt/SlotRealizationgt
lt/slotgt
lt/Constructiongt
lt/hasConstructiongt
lthasFrequency rdfvalue"8788" mlccorpus"PAROLE"
/gt lt/SyntacticFramegt
lt/hasSyntacticFramegt lt/SynUgt
lt/hasSynugt lt/Entrygt lt/rdfRDFgt
76
Entry Using Phrase
ltEntry rdfID"eat1"gt lthasSynu
rdfparseType"Resource"gt ltSynU
rdfID"eat1-SynU"gt ltexamplegtJohn ate
the cakelt/examplegt lthasSyntacticFramegt
ltSyntacticFrame rdfID"eat1SynFrame"gt
lthasSelfgt ltSelf
rdfID"eat1Self"gt ltheadedBy
rdfresource
"http//www.cs.vassar.edu/ide/rdf/isle-datcats/Ph
rasesVauxhave"/gt lt/Selfgt
lt/hasSelfgt
lthasConstructiongt ltConstruction
rdfID"eat1Const"gt
ltslotgt ltSlotRealization
rdfID"NPsubj"gt
lthasFunction rdfvalue"Subj"/gt
ltfilledBy rdfresource
"http//www.cs.vassar.edu/ide/rdf/isle-dat
cats/PhrasesNP"/gt
lt/SlotRealizationgt lt/slotgt
ltslotgt
ltSlotRealization rdfID"NPobj"gt
lthasFunction rdfvalue"Obj"/gt
ltfilledBy rdfresource
"http//www.cs.vassar.edu/ide/rdf/i
sle-datcats/PhrasesNP"/gt
lt/SlotRealizationgt lt/slotgt
lt/Constructiongt
lt/hasConstructiongt
lthasFrequency rdfvalue"8788" mlccorpus"PAROLE"
/gt lt/SyntacticFramegt
lt/hasSyntacticFramegt lt/SynUgt
lt/hasSynugt lt/Entrygt
77
Entry Using Construction
ltEntry rdfID"eat1"gt lthasSynu rdfparseType"Reso
urce"gt ltSynU rdfID"eat1-SynU"gt
ltexamplegtJohn ate the cakelt/examplegt
lthasSyntacticFramegt ltSyntacticFrame
rdfID"eat1SynFrame"gt
lthasSelfgt ltSelf
rdfID"eat1Self"gt ltheadedBy
rdfresource
"http//www.cs.vassar.edu/ide/rdf/isle-d
atcats/PhrasesVauxhave"/gt
lt/Selfgt lt/hasSelfgt
lthasConstruction rdfresource
"http//www.cs.vassar.edu/ide/rdf/isle-datcats/Co
nstructionsTransIntrans"/gt
lthasFrequency rdfvalue"8788" mlccorpus"PAROLE"
/gt lt/SyntacticFramegt
lt/hasSyntacticFramegt lt/SynUgt
lt/hasSynugt lt/Entrygt
78
Semantic Representation

• The data model underlying RDF/UML, etc. is
  universal, abstract enough to capture all types
  of info
• Semantic representations
• Registry of basic data categories
• meta-categories addressee, utterance, etc.
• Information categories eyebrow movement,
  gestures, pitch,
• Supporting ONTOLOGY of information categories
• Interpretative procedures yield another level of
  meaning represent.
• Registry of categories.

UNINTERPRETED REPRESENATION
INTERPRETED REPRESENTATION
INTERPRETATION PROCESS
79
MILE Lexical Data Category Registry (MDC)

• Instantiation of pre-defined lexical objects
• Extension of the shared class schema with
  lexicon-specific sub-classes and sub-properties
• Can be used off the shelf or as a departure
  point for the definition of new or modified
  categories
• Enables modular specification of lexical entities
• eliminate redundancy
• identify lexical entries or sub-entries with
  shared properties

80
MLC in RDF/S features
features are properties of lexical objects
mlmLexObject
mlmValues
mlmfeature
rdfssubPropertyOf
rdfssubClassOf
mlmsemFeature
rdfssubClassOf
mlmSemValues
mlmsynFeature
mlmSynValues
81
MLC in RDF/S syntactic features
ltrdfsProperty rdfIDsynCat"gt ltrdfssubProperty
Of rdfresource"http//webilc.ilc.cnr.it/lenc
i/isle/mile- schema-v.1synFeature"/gt ltrdfsrang
e rdfresourcehttp//webilc.ilc.cnr.it/lenci/
isle/mile- schema-v.1SynCatValues/gt lt/rdfsProp
ertygt ltrdfsClass rdfIDSynCatValuesgt ltrdfss
ubClassOf rdfresourcehttp//webilc.ilc.cnr.it
/lenci/isle/mile- schema-v.1 SynValues/gt
ltowloneOf rdfparseType"Collection"gt ltowlThin
g rdfabout"Noun"/gt ltowlThing
rdfabout"Verb"/gt ltowlThing
rdfabout"Adjective"/gt ... lt/owloneOfgt
lt/rdfsClassgt lt/rdfsRDFgt
feature values
82
MLC in RDF/S semantic features
ltrdfsProperty rdfIDdomain"gt ltrdfssubProperty
Of rdfresource"http//webilc.ilc.cnr.it/lenc
i/isle/mile- schema-v.1semFeature"/gt ltrdfsrang
e rdfresourcehttp//webilc.ilc.cnr.it/lenci/
isle/mile- schema-v.1 DomainValues/gt lt/rdfsPro
pertygt ltrdfsClass rdfIDDomainValuesgt ltrdfs
subClassOf rdfresourcehttp//webilc.ilc
.cnr.it/lenci/isle/mile- schema-v.1SemValues/gt
ltowloneOf rdfparseType"Collection"gt ltowl
Thing rdfabout"Finance"/gt ltowlThing
rdfabout"Medicine"/gt ltowlThing
rdfabout"Sport"/gt ... lt/owloneOfgt
lt/rdfsClassgt lt/rdfsRDFgt
domain ontology
83
Synsets in RDF/S
mlmword
mlmSynset
rdfsliteral
mlmgloss
rdfsliteral
mlmfeature
mlmsynsetRelation
mlmValues
mlmSynset
cf. also http//www.semanticweb.org/library/wordne
t/wordnet-20000620.rdfs
84
Synsets in RDF/S
ltrdfsClass rdfID"Synset"gt ltrdfslabelgtSynsetlt/
rdfslabelgt ltrdfscommentgtThis class formalizes
the notion of synset as defined in WordNet
(Fellbaum 1998).lt/rdfscommentgt ltrdfssubClassOf
rdfresourceLexObject/gt lt/rdfsClassgt ltrdfsP
roperty rdfID"synsetRelation"gt ltrdfsdomain
rdfresource"Synset"/gt ltrdfsrange
rdfresource"Synset"/gt lt/rdfsPropertygt ltrdfsP
roperty rdfID"hypernym" mlmsource"WordNet1.7"gt
ltrdfscommentgtThe WordNet hypernym
relationlt/rdfscommentgt ltrdfssubPropertyOf
rdfresource"synsetRelation"/gt lt/rdfsPropertygt
ltrdfsProperty rdfID"meronym"
mlmsource"WordNet1.7"gt ltrdfscommentgtThe
WordNet meronym relationlt/rdfscommentgt ltrdfssub
PropertyOf rdfresource"synsetRelation"/gt lt/rdfs
Propertygt
relation between synsets
different types of synset relations
85
WordNet 1.7 Synsets
ltmlmSynset rdfabout"http//www.cogsci.prin
ceton.edu/wn1.7/concept01752990
mlmsource"WordNet1.7"gt ltmlmglossgtA member of
the genus Canislt/mlmglossgt ltmlmwordgtdoglt/mlmwo
rdgt ltmlmwordgtdomestic doglt/mlmwordgt ltmlmwordgt
Canis familiarislt/mlmwordgt ltmdcsynCat
rdfresource"Noun"/gt ltmdcdomain
rdfresource"Zoology"/gt ltmdchypernym rdfreso
urce"http//www.cogsci.princeton.edu/wn1.7/conce
pt 01752283"/gt lt/mlmSynsetgt
features
hypernym
86
Foundations of the Mapping Experiment
87
1. The MILE building-block model

• The MILE Lexical Classes and the MILE Lexical
  Data Categories are the main building blocks of
  the MILE lexical architecture
• Building blocks allow two kinds of reusability
• intra-lexicon reusability (within the same
  lexicon)
• inter-lexicon reusability (among different
  lexicons)

88
How building-blocks work?
89
2. MILE a meta-entry

• MILE is
• a general schema for multilingual lexical
  resources
• a lexical meta-entry, a common representational
  layer for multilingual lexicons
• Computational lexicons can be viewed as different
  instances of the MILE schema

MILE
lexicon1
lexicon3
lexicon2
90
MILE and Content Interoperability

• This common shared compatible representation of
  lexical objects is particularly suited to
• manipulate objects available in different lexical
  resources
• understand their deep semantics
• apply the same operations to lexical objects of
  the same type
• key elements of Content Interoperability

91
The Mapping Experiment Why?

• It is a concrete experiment aimed to test the
  expressive potentialities and capabilities of the
  MILE
• The idea is that if the MILE atomic notions
  combined together in different ways suit the
  different visions underlying two lexicons such
  as FrameNet and NOMLEX,
• the MILE will come out fortified
• its adoption as an interface between differently
  conceived lexical architectures can be pushed
  more
• key issues for content interoperability between
  resources can be addressed

92
The mapping scenarios

• High level mapping of the objects of a lexicon
  into the objects of the abstract model
• ? the native structure is maintained and no
  format conversion is performed
• Translate instances of lexical entries directly
  in MILE
• ? acts as a true interchange format

93
FrameNet to MILE
94
FrameNet-MILE Observations

• The mapping is promising
• Frame ? Predicate (primitive)
• Frame Elements ? Argument (enlarge the set of
  possible values)
• Lexical_Unit ? SemU
• Link SemU-Predicate (obligatory) should become
  underspecified
• But
• Lack of inheritance mechanism in the Predicate
  does not allow to represent the hierarchical
  organization of Frames and Sub-frames, temporal
  ordering among Frames, subsumption relations
  among Frames
• We could add a new object PredicateRelation to
  allow for the description of relations occurring
  between predicates and sub-predicates

95
MLCSynU
MLCSemU
MLCSemanticFrame TypeOfLinkAgentnom IncludedArg
0
MLCCorrespSynUSemU
MLCPredicate
MILE
MLCArgument
MLCArgument
nom-type ((subject))
NOMLEX
96
NOMLEX-MILE Observations

• The mapping is promising
• Notions represented in NOMLEX have a
  correspondent in MILE
• But ..
• are expressed with two opposite lexical
  structures
• In NOMLEX,
• lexical information is expressed in a very
  compact way
• no clear cut boundaries between the levels of
  linguistic description
• In MILE
• compressed info should be decompressed and spread
  over different MILE lexical layers and objects
  SynU, SemU, SemanticFrame with its Predicate and
  relevant Arguments to account for the
  incorporation of the Agent.

97
Lesson Learned from the mapping

• The results of the experiments are promising
• FrameNet offers the possibility to be confronted
  with two similar lexical models, but not
  perfectly overlapping lexical objects test
  the adequacy of the linguistic objects
• NOMLEX gives the opportunity to work with two
  lexicons where linguistic notions correspond but
  are expressed with an opposite lexicon structure
  test the adequacy of the architectural
  model
• The high granularity and modularity of MILE
• allow the compatibility with differently packaged
  linguistic objects
• allow the addition of new objects and relations
  without perverting the general architecture

98
RDF and MILE Why?

• Some reasons (from Nancy Ide et al. 2003)
• MILE as a hierarchy of lexical objects built up
  by combining data categories via clearly defined
  relations is an ideal structure for rendering in
  RDF
• RDF mechanism, with the capacity of expressing
  named relations between objects, offers a
  web-based means to represent the MILE
  architecture
• RDF representation of linguistic information is
  an invaluable resource for language processing
  applications in the Semantic Web
• RDF description and instantiation is in line with
  the goal of ISO TC37 SC4

99
RDF Representation of MILE

• MILE was already supplied with
• an RDF schema for the MILE Syntactic Layer
• an instantiation of pre-defined syntactic objects
• We increased the repository of shared lexical
  objects with the RDF description and (partial!)
  instantiations of the objects of the semantic and
  linking layers
• This has been carried out with the intent to
• be submitted within the ISO TC37/SC4
• foster the adoption of MILE, by offering a
  library of RDF objects ready-to-use

100
An RDF Schema for the synt-sem linking
lt!-- An RDF Schema for ISLE lexical
entries v 0.1 2004/05/05 Author
Monachini --gt ltrdfRDF xmlnsrdf"http//www.w3.or
g/1999/02/22-rdf-syntax-ns"
xmlnsrdfs"http//www.w3.org/2000/01/rdf-schema"
xmlnsowl "http//www.w3.org/2002/07/ow
l xmlnsmlc "http//www.cs.vassar.edu/
ide/rdf/isle-schema-v.6 xmlnsmlc
"http//www.ilc.cnr.it/clips/rdf/isle-schema-synt
semlinking_v.1"gt lt!-- ISLE/MILE lexical
objects (classes for the synt-sem linking)
--gt ltrdfsClass rdfabout"http//www.ilc.cnr.it/
clips/rdf/isle-schema-syntsemlinking_v.1CorrespSy
nUSemU"gt ltrdfslabelgtCorrespSynUSemUlt/rdfslabelgt
ltrdfscommentgtThis class links a SynU to a
SemUlt/rdfscommentgt lt/rdfsClassgt ltrdfsClass
rdfabout"http//www.ilc.cnr.it/clips/rdf/isle-sc
hema-syntsemlinking_v.1PredicativeCorresp"gt
ltrdfslabelgtPredicativeCorresplt/rdfslabelgt
ltrdfscommentgtThis class contains the
associations between the syntactic slots and
semantic argumentlt/rdfscommentgt lt/rdfsClassgt
ltrdfsClass rdfabout"http//www.ilc.cnr.it/cli
ps/rdf/isle-schema-syntsemlinking_v.1SlotArgCorre
sp"gt ltrdfslabelgtSlotArgCorresplt/rdfslabelgt
ltrdfscommentgtThis class links a syntactic slots
to a semantic argumentlt/rdfscommentgt
lt/rdfsClassgt
Classes
101
An RDF Schema for the synt-sem linking
lt!-- Properties (relations) between objects and
between objects and atomic values
--gt ltrdfProperty rdfabout"http//www.ilc.cnr.i
t/clips/rdf/isle-schema-syntsemlinking_v.1hasSour
ceSynU"gt ltrdfslabelgthasSourceSynUlt/rdfslabelgt
ltrdfsdomain rdfresource"http//www.ilc.cnr.it/
clips/rdf/isle-schema-syntsemlinking_v.1CorrespSy
nUSemU"/gt ltrdfsrange rdfresource"http//www.cs
.vassar.edu/ide/rdf/isle-schema-v.6SynU"/gt
lt/rdfPropertygt ltrdfProperty
rdfabout"http//www.ilc.cnr.it/clips/rdf/isle-sc
hema-syntsemlinking_v.1hasTargetSemU"gt
ltrdfslabelgthasTargetSemUlt/rdfslabelgt
ltrdfsdomain rdfresource"http//www.ilc.cnr.it/
clips/rdf/isle-schema-syntsemlinking_v.1CorrespSy
nUSemU"/gt ltrdfsrange rdfresource"http//www.cs
.vassar.edu/ide/rdf/isle-schema-v.6SemU"/gt
lt/rdfPropertygt ltrdfProperty
rdfabout"http//www.ilc.cnr.it/clips/rdf/isle-sc
hema-syntsemlinking_v.1hasPredicativeCorresp"gt
ltrdfslabelgthasPredicativeCorresplt/rdfslabelgt
ltrdfsdomain rdfresource"http//www.ilc.cnr.it/
clips/rdf/isle-schema-syntsemlinking_v.1CorrespSy
nUSemU"/gt ltrdfsrange rdfresource"http//www.il
c.cnr.it/clips/rdf/isle-schema-syntsemlinking_v.1
PredicativeCorresp"/gt lt/rdfPropertygt
ltrdfProperty rdfabout"http//www.ilc.cnr.it/c
lips/rdf/isle-schema-syntsemlinking_v.1includesSl
otArgCorresp"gt ltrdfslabelgtincludesSlotArgCorresp
lt/rdfslabelgt ltrdfsdomain rdfresource"http//w
ww.ilc.cnr.it/clips/rdf/isle-schema-syntsemlinking
_v.1PredicativeCorresp"/gt ltrdfsrange
rdfresource"http//www.ilc.cnr.it/clips/rdf/isle
-schema-syntsemlinking_v.1SlotArgCorresp"/gt
lt/rdfPropertygt
Properties
102
The library of Pre-instantiated objects

• Enable modular specification of lexical entities
• eliminate redundancy
• identify lexical entries or sub-entries with
  shared properties
• create ready-to-use packages that can be combined
  in different ways
• Can be used off the shelf or as a departure
  point for the definition of new or modified
  categories

103
MDCR for some objects

• lt!-- Sample LDCR entry for a PredicativeCorresp
  and SlotArgCorresp objects
• DataCats for ISLE lexical entries
• v 0.1 2004/05/17
• Author Monachini --gt
• ltrdfRDF xmlnsrdf"http//www.w3.org/1999/02/22-r
  df-syntax-ns"
• ltPredicativeCorresp rdfID"isobivalent"gt
• ltincludesSlotArgCorresp
• rdfresourcehttp//www.ilc.cnr.it/clips/rdf
  /isle-datacats/SlotArgCorrespArg0Slot0
  Arg1Slot1/gt
• lt/includesSlotArgCorrespgt
• lt/PredicativeCorrespgt
• ltSlotArgCorresp rdfID"Arg0Slot0"
• SlotNumber"0"
• ArgNumber"0"gt
• lt/SlotArgCorrespgt
• ltSlotArgCorresp rdfID"Arg1Slot1"

Pre-instantiated PredicativeCorresp
Pre-instantiated SlotArgCorresp
104
A Sample Entry in MILE

• The entry is shown in a double alternative
• the full specification of a lexical object
  PredicativeCorresp
• an already instantiated object PredicativeCorresp
• The advantage is that
• the object does not need to be specified in the
  entry
• and can be used and reused in other entries
• explore the potential of MILE for representation
  of lexical data

105
Sample full entry for amareV

• lt!-- The SynU SemU link --gt
• ltcorrespondsTogt
• ltCorrespSynUSemUgt
• lthasSourceSynU mlcpID"SYNUamareV"gt
• lt/hasSourceSynUgt
• lthasTargetSemU mlcpID"SEMUamareEXPEVE"
  gt
• lt/hasTargetSemUgt
• lthasPredicativeCorrespgt
• ltPredicativeCorresp
  mlcpID"amare-PredCorresp"gt
• ltincludesSlotArgCorrespgt
• ltSlotArgCorresp SlotNumber"0"
  ArgNumber"0"gt
• lt/SlotArgCorrespgt
• 
  ltSlotArgCorresp SlotNumber"1" ArgNumber"1"gt
• lt/SlotArgCorrespgt
• lt/includesSlotArgCorrespgt
• lt/PredicativeCorrespgt
• lt/hasPredicativeCorrespgt
• lt/CorrespSynUSemUgt
• lt/correspondsTogt

The full object PredicativeCorresp
106
the abbreviated entry

• lt!-- The SynU SemU link --gt
• ltcorrespondsTogt
• ltCorrespSynUSemUgt
• lthasSourceSynU mlcpID"SYNUamareV"gt
• lt/hasSourceSynUgt
• lthasTargetSemU mlcpID"SEMUamareEXPEVE
  "gt lt/hasTargetSemUgt
• lthasPredicativeCorresp
• rdfresourcehttp//www.ilc.cnr.it/cli
  ps/rdf/isle-datacats/PredicativeCorrespisobivalen
  t/gt
• lt/CorrespSynUSemUgt
• lt/correspondsTogt
• lt/SynUgt
• lt/hasSynugt

Instantiated object PredicativeCorresp
107
The RDF Schema, the DCR for MILE objects and the
entries are available at www.ilc.cnr.it/clips/rdf/

108
and INTERA?

• INTERA Multilingual Terminological Lexica will
  follow and merge the two frameworks
• The MILE and
• ISO TMF (Terminological Markup Framework)

109
Beyond MILE future work

• MILE Lexical Model oriented towards an
• Open Distributed Lexical Infrastructure
• Lexical Information Servers for multiple access
  to lexical information repositories
• Enhance
• user-adaptivity
• resource sharing
• cooperative creation
• Develop integration and interchange tools

110
Broadening MILE ... other
languages

• Ongoing enlargement to Asian languages (Chinese,
  Japanese, Korean, Thai, Hindi ...)
• promote common initiatives between Asia Europe
  (e.g. within the EU 6th FP)
• The creation of an Open Distributed Lexical
  Infrastructure, also supported by Asian
  Institutions
• AFNLP
• University of Tokyo (Dept. of Computer Science)
• Korean KAIST and KORTERM
• Academia Sinica (Taiwan)

To valorise results increase visibility of LR
standardisation initiatives in a world-wide
context, while concretely promoting the
launching of a new common platform for
multilingual LR creation management
111
Using semantically tagged corpora to acquire
semantic info and enhance Lexicons

• evaluate the disambiguating power of the semantic
  types of the lexicon
• assess the need of integrating lexicons with
  attested senses and/or phraseology
• identify the inadequacy of sense distinctions in
  lexicons
• check actual frequency of known senses in
  different text types
• have a more precise and complete view on the
  semantics of a lemma
• identify the most general senses
• capture the most specific shifts of meaning
• Capture just the core, basic distinctions in a
  core lexicon
• Corpus analysis must not lead to excessive
  granularity of sense distinctions, but draw a
  distinction between
• sense discrimination to be kept under control
  - clustering (manually or automatically)
• additional, more granular information (often of
  collocational nature) which can/must be
  acquired/encoded within the broader senses, e.g.
  to help translation

112
Dynamic lexicon

• Current computational lexicons (even WordNets)
  are static objects, still shaped on traditional
  dictionaries
• suffering from the limitations induced by paper
  support
• Thinking at the complex relationships between
  lexicon and corpus
• towards a flexible model of dynamic lexicon
• extending the expressiveness of a core static
  lexicon adapting to the requirements of language
  in use as attested in corpora
• with semantic clustering techniques, etc.
• Convert the extreme flexibility
  multidimensionality of meaning into large-scale
  and exploitable (VIRTUAL?) resources

a Lexicon and Corpus together
113
What to annotate?

• Mix of
• Word-sense annotation (implicit semantic markup)
• Semantic/conceptual markup
• Syntagmatic relations
• Dependency relations
• Semantic roles

114
Need for a common Encoding Policy ?

• Agree on common policy issues?
• is it feasible?
• desirable?
• to what extent?
• This would imply, among others
• analysis of needs also applicative/industrial -
  before any large development initiative
• base semantic tagging on commonly accepted
  standards/guidelines ??
• up to which level?
• Common semantic tagset Gold Standard??
• build a core set of semantically tagged corpora,
  encoded in a harmonised way, for a number of
  languages??
• make annotated corpora available to the community
  by large
• involve the community, collect and analyse
  existing semantically tagged corpora
• devise common set of parameters for analysis

115
A few Issues for discussion MILE lexicon
standards More standardisation initiatives?

• MILE - a general schema for encoding multilingual
  lexical info, as a meta-entry, as a common
  representational layer
• Short medium term requirements wrt standards
  for multilingual lexicons and content encoding,
  also industrial requirements
• Relation with Spoken language community (see
  ELRA)
• Semantic Web standards the needs of content
  processing technologies importance of reaching
  consensus on (linguistic non-linguistic)
  content, in addition to agreement on formats
  encoding issues (words convey content
  knowledge)
• Define further steps necessary to converge on
  common priorities

116
Broadening MILE ... other communities

• NLP, lexicons, terminologies, ontologies,
  Semantic Web
• a continuum?
• Knowledge management is critical.
• For content interoperability, need to converge
  around agreed standards also for the
  semantic/conceptual level
• is the field mature enough to converge around
  agreed standards also for the semantic/conceptual
  level (e.g. to automatically establish links
  among different languages)?
• Is the field of multilingual lexical resources
  ready to tackle the challenges set by the
  Semantic Web development?
• Foster better integration with
• corpus-driven data
• terminology/ontology/semantic web communities
• multimodal multimedial aspects

Oriented towards open, distributed lexical
resources Lexical Information Servers for
multiple access to lexical information
repositories
117
A few Issues for discussion NLP, lexicons,
content, ontologies, Semantic Web a
continuum?

• Need for robust systems, able to acquire/tune
  multilingual lexical/linguistic/