MILE 0409

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Published on April 21, 2008

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Slide1:  Infrastructural Language Resources & Standards for Multilingual Computational Lexicons Nicoletta Calzolari … with many others Istituto di Linguistica Computazionale - CNR - Pisa glottolo@ilc.cnr.it The ENABLER Mission:  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 towards ….:  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 R&D & 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 Lexicon and Corpus: a multi-faceted interaction:  Lexicon and Corpus: a 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 ... ...Language as a “Continuum”:  ...Language as a “Continuum” 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 Lexicon & Corpus as two viewpoints on the same ling. object …. even more in a multilingual context Extraction from texts vs. formal representation in lexicons:  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: it’s 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 Correlation between different levels of linguistic description in the design of a lexical entry:  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 towards Corpus based Semantic Lexicons … at least in principle:  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/... Slide9:  But … they will never be “complete” Semantic networks: Euro-/ItalWordNet Lexicons: PAROLE/SIMPLE/CLIPS TreeBanks Infrastructure of Language Resources... Lexical acquisition systems (syntactic & semantic) from corpora Infrastructure of tools Robust morphosyntactic & syntactic analysers Word-sense disambiguation systems Sense classifiers ... ...static …dynamic International Standards Slide10:  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 (ILI=Princeton 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, ... Slide11:  EuroWordNet Multilingual Data Structure Slide12:  {Casa, abitazione, dimora } Hyperonym: {edificio,..} Hyponym: {villetta } {catapecchia, bicocca, .. } {cottage} {bungalow } Role_location: {stare, abitare, ...} Role_target_direction: {rincasare} Role_patient: {affitto, locazione} Mero_part: {vestibolo} {stanza} Holo_part: {casale} {frazione} {caseggiato} home, domicile, .. house TOP Concepts:Object,Artifact,Building Synsets linked by Semantic Relations in ItalWordNet Jur-WordNet:  Jur-WordNet With ITTG-CNR (Istituto di Teoria e Tecniche dell’informazione 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. Terminological Lexicon of Navigation & Sea Transportation:  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 Slide15:  PAROLE Ital. Synt. Lex. ’96-’98 SIMPLE Ital. Sem. Lex. ’98-2000 CLIPS 2000-2004 morphology: 20,000 entries syntax: 20,000 words semantics: 10,000 senses phonology morphology 55,000 words syntax semantics: 55,000 senses SGML SGML XML PAROLE/SIMPLE 12 harmonised computational lexicons http://www.ilc.cnr.it/clips/ Slide16:  machine language learning Slide17:  machine language learning development of conceptual networks linguistic learning adaptive classification systems information extraction bootstrapping of grammars linguistic change models language usage models bootstrapping of lexical information Slide18:  lexica unstructured text data annotation tools annotated data machine learning for linguistic knowledge acquisition lexica cross-lingual information retrieval multi-lingual information extraction multi-lingual text mining user needs lexicon model Architecture for linguistic knowledge acquisition ... LKG …. towards “dynamic” lexicons, able to auto-enrich terminology Slide19:  Harmonisation: More & more Need of a Global View for 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 A short guide to ISLE/EAGLES http://www.ilc.cnr.it/EAGLES96/isle/ISLE_Home_Page.htm :  A short guide to ISLE/EAGLES http://www.ilc.cnr.it/EAGLES96/isle/ISLE_Home_Page.htm Multilingual Computational Lexicon Working Group Target: … the Multilingual ISLE Lexical Entry (MILE):  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 Paths to Discover the Basic Notions of MILE:  Paths to Discover the Basic 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 … Designing MILE Steps towards MILE: :  Designing MILE Steps 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 The MILE Basic Notions (the EAGLES/ISLE CLWG):  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) …. The MILE Lexical Classes – Data Categories for Content Interoperability:  The MILE Lexical Classes – Data Categories for Content Interoperability Francesca Bertagna*, Alessandro Lenci°, Monica Monachini*, Nicoletta Calzolari* *ILC–CNR – Pisa °Pisa University Overview:  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 … The MILE Lexical Model:  MILE Lexical Model The MILE Lexical Model Guidelines syntactic semantic lexicons … where after? The MILE Main Features:  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 The MILE Lexical Model (MLM):  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 lexicon#1 lexicon#3 lexicon#2 MILE the building-block model:  MILE the 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) MILE the building-block model:  MILE the building-block model Modularity in MILE:  Modularity in MILE multilingual correspondence conditions multiple levels of modularity Horizontal organization, where independent, but interlinked, modules allow to express different dimensions of lexical entries The Mono-MILE:  The Mono-MILE Each monolingual layer within Mono-MILE identifies a basic unit of lexical description morphological layer MU basic unit to describe the inflectional and derivational morphological properties of the word syntactic layer SynU basic unit to describe the syntactic behaviour of the MU semantic layer SemU basic unit to describe the semantic properties of the MU The Mono-MILE:  The Mono-MILE MU Within each layer, a basic linguistic information unit is identified Granularity in MILE:  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 Defining the MLM:  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) The MILE Lexical Objects:  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 The MILE E/R diagrams:  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 MILE Lexical Objects: Syntactic Layer:  MILE Lexical Objects: Syntactic Layer hasSyntacticFrame hasFrameSet composedby correspondTo 1..* * * * Slide40:  … expanding one node. … … Slide41:  belongsToSynset hasSemFrame hasSemFeature hasCollocation semanticRelation MILE Lexical Objects: Semantic Layer * 0..1 * * * Slide42:  hasSourceSynu hasTargetSemu hasPredicativeCorresp IncludesSlotArgCorresp MILE Lexical Objects: Synt-Sem Linking 1 1 1 0..* Syntax-Semantics Linking:  Syntax-Semantics Linking Slot0:Arg1 Slot1:Arg0 Syntax-Semantics Linking:  Syntax-Semantics Linking John gave the book to Mary John gave Mary the book SynU#1 obj_NP obl_PP_to SemU#1 Semantic_Frame:GIVE Arg1 Agent subj_NP SynU#2 obj_NP obj_NP subj_NP Arg2 Theme Arg3 Goal Slide45:  CorrespSynUSemU Syntax-Semantic Linking in SIMPLE Transitive structure Slot0 Slot1 SemU1_migliorare SemU2_migliorare CHANGE_OF_STATE CAUSE_CHANGE_OF_STATE PRED_ migliorare ARG0:Agent ARG1:Patient isomorphic non-isomorphic Frameset Intransitive structure Slot0 Ø CorrespSynUSemU SlotArgCorresp SlotArgCorresp Slide46:  hasMUMUCorr hasSynUSynuCorr hasSemUSemUCorr hasSynsetMultCorr hasSemFrameCorr The Multilingual layer 1..0 1..0 1..0 1..0 1..0 MILE approach to multilinguality:  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.) The Multi-MILE:  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 The Multi-MILE:  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 Multi-MILE:  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] Multi-MILE:  Multi-MILE dito finger toe modif(mano) modif(piede) multilingual conditions run + PP_into entrare “to enter” +PP_di_corsa multilingual conditions IT Lexicon EN Lexicon MILE Lexical Classes:  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 MILE Lexical Classes an ontology of lexical objects:  MILE Lexical Classes an ontology of lexical objects MILE Lexical Data Categories:  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 The MILE Data Categories:  User-defined MDC The MILE Data Categories Instances of the MILE Lexical Classes are Data Categories MDC can belong to a shared repository or be user-defined Core MDC The MILE Data Categories User-adaptability and extensibility:  The MILE Data Categories User-adaptability and extensibility HUMAN ARTIFACT EVENT ANIMAL GROUP AGE MAMMAL instance_of Core UserDefined MILE Lexical Data Categories:  MILE Lexical Data Categories MLM:Feature MLM:GrammaticalFunction MILE Lexical Operations:  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 Multilingual Operations:  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 Defining the MLM:  Defining the MLM MILE Entry Schema MILE Lexical Classes RDF/S Descriptions RDF Instantiation of the MLM:  RDF Instantiation of the MLM Lexicon#1 Lexicon#2 Lexicon#3 Resources Lexical Objects Lexical Classes Lexical Data Categories Resources Metadata MILE Lexical Model:  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 User-Adaptability and Resource Sharing in MILE:  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 … The MILE Lexical Model:  The MILE Lexical Model MILE Lexical Model RDF Instantiation of the MLM:  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 The RDF Schema:  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 Goals:  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 Advantages of RDF:  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 Example:  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 Sample Repositories:  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 Slide71:  <rdfs:Class rdf:about="http://www.cs.vassar.edu/~ide/rdf/isle-enumerated-classes#FunctionType"> <owl:oneOf> <rdf:Seq> <rdf:li>Subj</rdf:li> <rdf:li>Obj</rdf:li> <rdf:li>Comp</rdf:li> <rdf:li>Arg</rdf:li> <rdf:li>Iobj</rdf:li> </rdf:Seq> </owl:oneOf> </rdfs:Class> <rdfs:Class rdf:about="http://www.cs.vassar.edu/~ide/rdf/isle-enumerated-classes#SynFeatureName"> <owl:oneOf> <rdf:Seq> <rdf:li>tense</rdf:li> <rdf:li>gender</rdf:li> <rdf:li>control</rdf:li> <rdf:li>person</rdf:li> <rdf:li>aux</rdf:li> </rdf:Seq> </owl:oneOf> </rdfs:Class> <rdfs:Class rdf:about="http://www.cs.vassar.edu/~ide/rdf/isle-enumerated-classes#SynFeatureValue"> <owl:oneOf> <rdf:Seq> <rdf:li>have</rdf:li> <rdf:li>be</rdf:li> <rdf:li>subject_control</rdf:li> <rdf:li>object_control</rdf:li> <rdf:li>masculine</rdf:li> <rdf:li>feminine</rdf:li> </rdf:Seq> </owl:oneOf> </rdfs:Class> Enumerated classes Sample LDCR for a Phrase Object:  <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:mlc="http://www.cs.vassar.edu/~ide/rdf/isle-schema-v.6#"> <Phrase rdf:ID="NP" rdfs:label="NP"/> <Phrase rdf:ID="Vauxhave"> <hasSynFeature> <SynFeature> <hasSynFeatureName rdf:value="aux"/> <hasSynFeatureValue rdf:value="have"/> </SynFeature> </hasSynFeature> </Phrase> </rdf:RDF> Sample LDCR for a Phrase Object Sample LDCR entry for a Construction object:  Sample LDCR entry for a Construction object <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns="http://www.cs.vassar.edu/~ide/rdf/isle-schema-v.6#"> <Construction rdf:ID="TransIntrans"> <slot> <SlotRealization rdf:ID="NPsubj"> <hasFunction rdf:value="Subj"/> <filledBy rdf:resource= "http://www.cs.vassar.edu/~ide/rdf/isle-datcats/Phrases#NP"/> </SlotRealization> </slot> <slot> <SlotRealization rdf:ID="NPobj"> <hasFunction rdf:value="Obj"/> <filledBy rdf:resource= "http://www.cs.vassar.edu/~ide/rdf/isle-datcats/Phrases#NP"/> </SlotRealization> </slot> </Construction> </rdf:RDF> Full entry:  Full entry <Entry rdf:ID="eat1"> <hasSynu rdf:parseType="Resource"> <SynU rdf:ID="eat1-SynU"> <example>John ate the cake</example> <hasSyntacticFrame> <SyntacticFrame rdf:ID="eat1SynFrame"> <hasSelf> <Self rdf:ID="eat1Self"> <headedBy> <Phrase rdf:ID="Vauxhave"> <hasSynFeature> <SynFeature> <hasSynFeatureName rdf:value="aux"/> <hasSynFeatureValue rdf:value="have"/> </SynFeature> </hasSynFeature> </Phrase> </headedBy> </Self> </hasSelf> Continued… Slide75:  Continued from previous slide… <hasConstruction> <Construction rdf:ID="eat1Const"> <slot> <SlotRealization rdf:ID="NPsubj"> <hasFunction rdf:value="Subj"/> <filledBy rdf:value="NP"/> </SlotRealization> </slot> <slot> <SlotRealization rdf:ID="NPobj"> <hasFunction rdf:value="Obj"/> <filledBy rdf:value="NP"/> </SlotRealization> </slot> </Construction> </hasConstruction> <hasFrequency rdf:value="8788" mlc:corpus="PAROLE"/> </SyntacticFrame> </hasSyntacticFrame> </SynU> </hasSynu> </Entry> </rdf:RDF> Entry Using Phrase:  Entry Using Phrase <Entry rdf:ID="eat1"> <hasSynu rdf:parseType="Resource"> <SynU rdf:ID="eat1-SynU"> <example>John ate the cake</example> <hasSyntacticFrame> <SyntacticFrame rdf:ID="eat1SynFrame"> <hasSelf> <Self rdf:ID="eat1Self"> <headedBy rdf:resource= "http://www.cs.vassar.edu/~ide/rdf/isle-datcats/Phrases#Vauxhave"/> </Self> </hasSelf> <hasConstruction> <Construction rdf:ID="eat1Const"> <slot> <SlotRealization rdf:ID="NPsubj"> <hasFunction rdf:value="Subj"/> <filledBy rdf:resource= "http://www.cs.vassar.edu/~ide/rdf/isle-datcats/Phrases#NP"/> </SlotRealization> </slot> <slot> <SlotRealization rdf:ID="NPobj"> <hasFunction rdf:value="Obj"/> <filledBy rdf:resource= "http://www.cs.vassar.edu/~ide/rdf/isle-datcats/Phrases#NP"/> </SlotRealization> </slot> </Construction> </hasConstruction> <hasFrequency rdf:value="8788" mlc:corpus="PAROLE"/> </SyntacticFrame> </hasSyntacticFrame> </SynU> </hasSynu> </Entry> Entry Using Construction:  Entry Using Construction <Entry rdf:ID="eat1"> <hasSynu rdf:parseType="Resource"> <SynU rdf:ID="eat1-SynU"> <example>John ate the cake</example> <hasSyntacticFrame> <SyntacticFrame rdf:ID="eat1SynFrame"> <hasSelf> <Self rdf:ID="eat1Self"> <headedBy rdf:resource= "http://www.cs.vassar.edu/~ide/rdf/isle-datcats/Phrases#Vauxhave"/> </Self> </hasSelf> <hasConstruction rdf:resource= "http://www.cs.vassar.edu/~ide/rdf/isle-datcats/Constructions#TransIntrans"/> <hasFrequency rdf:value="8788" mlc:corpus="PAROLE"/> </SyntacticFrame> </hasSyntacticFrame> </SynU> </hasSynu> </Entry> Semantic Representation:  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 INTERPRETATION PROCESS INTERPRETED REPRESENTATION MILE Lexical Data Category Registry (MDC):  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 MLC in RDF/S features:  MLC in RDF/S features mlm:LexObject mlm:Values mlm:feature mlm:SemValues mlm:SynValues rdfs:subClassOf mlm:semFeature rdfs:subClassOf mlm:synFeature rdfs:subPropertyOf features are properties of lexical objects MLC in RDF/S syntactic features:  MLC in RDF/S syntactic features <rdfs:Property rdf:ID=“synCat"> <rdfs:subPropertyOf rdf:resource="http://webilc.ilc.cnr.it/~lenci/isle/mile- schema-v.1#synFeature"/> <rdfs:range rdf:resource=“http://webilc.ilc.cnr.it/~lenci/isle/mile- schema-v.1#SynCatValues”/> </rdfs:Property> <rdfs:Class rdf:ID=“SynCatValues”> <rdfs:subClassOf rdf:resource=“http://webilc.ilc.cnr.it/~lenci/isle/mile- schema-v.1 #SynValues”/> <owl:oneOf rdf:parseType="Collection"> <owl:Thing rdf:about="#Noun"/> <owl:Thing rdf:about="#Verb"/> <owl:Thing rdf:about="#Adjective"/> ... </owl:oneOf> </rdfs:Class> </rdfs:RDF> feature values MLC in RDF/S semantic features:  MLC in RDF/S semantic features <rdfs:Property rdf:ID=“domain"> <rdfs:subPropertyOf rdf:resource="http://webilc.ilc.cnr.it/~lenci/isle/mile- schema-v.1#semFeature"/> <rdfs:range rdf:resource=“http://webilc.ilc.cnr.it/~lenci/isle/mile- schema-v.1 #DomainValues”/> </rdfs:Property> <rdfs:Class rdf:ID=“DomainValues”> <rdfs:subClassOf rdf:resource=“http://webilc.ilc.cnr.it/~lenci/isle/mile- schema-v.1#SemValues”/> <owl:oneOf rdf:parseType="Collection"> <owl:Thing rdf:about="#Finance"/> <owl:Thing rdf:about="#Medicine"/> <owl:Thing rdf:about="#Sport"/> ... </owl:oneOf> </rdfs:Class> </rdfs:RDF> “domain ontology” Synsets in RDF/S:  Synsets in RDF/S mlm:Synset rdfs:literal mlm:word mlm:Synset mlm:synsetRelation mlm:Values rdfs:literal mlm:gloss mlm:feature cf. also http://www.semanticweb.org/library/wordnet/wordnet-20000620.rdfs Synsets in RDF/S:  <rdfs:Class rdf:ID="Synset"> <rdfs:label>Synset</rdfs:label> <rdfs:comment>This class formalizes the notion of synset as defined in WordNet (Fellbaum 1998).</rdfs:comment> <rdfs:subClassOf rdf:resource=“#LexObject”/> </rdfs:Class> <rdfs:Property rdf:ID="synsetRelation"> <rdfs:domain rdf:resource="#Synset"/> <rdfs:range rdf:resource="#Synset"/> </rdfs:Property> <rdfs:Property rdf:ID="hypernym" mlm:source="WordNet1.7"> <rdfs:comment>The WordNet hypernym relation</rdfs:comment> <rdfs:subPropertyOf rdf:resource="#synsetRelation"/> </rdfs:Property> <rdfs:Property rdf:ID="meronym" mlm:source="WordNet1.7"> <rdfs:comment>The WordNet meronym relation</rdfs:comment> <rdfs:subPropertyOf rdf:resource="#synsetRelation"/> </rdfs:Property> Synsets in RDF/S relation between synsets different types of synset relations WordNet 1.7 Synsets:  <mlm:Synset rdf:about="http://www.cogsci.princeton.edu/~wn1.7/concept#01752990“ mlm:source="WordNet1.7"> <mlm:gloss>A member of the genus Canis</mlm:gloss> <mlm:word>dog</mlm:word> <mlm:word>domestic dog</mlm:word> <mlm:word>Canis familiaris</mlm:word> <mdc:synCat rdf:resource="#Noun"/> <mdc:domain rdf:resource="#Zoology"/> <mdc:hypernym rdf:resource="http://www.cogsci.princeton.edu/~wn1.7/concept #01752283"/> </mlm:Synset> WordNet 1.7 Synsets features hypernym Foundations of the Mapping Experiment:  Foundations of the Mapping Experiment 1. The MILE building-block model:  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) How building-blocks work?:  How building-blocks work? 2. MILE: a meta-entry:  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 lexicon#1 lexicon#3 lexicon#2 MILE and Content Interoperability:  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 The Mapping Experiment: Why?:  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 The mapping scenarios:  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 FrameNet to MILE:  FrameNet to MILE FrameNet-MILE: Observations:  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 Slide95:  MLC:SynU MLC:SemU MLC:SemanticFrame TypeOfLinkAgentnom IncludedArg 0 MLC:Predicate MLC:Argument MLC:Argument MLC:CorrespSynUSemU :nom-type ((subject)) NOMLEX-MILE: Observations:  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. Lesson Learned from the mapping:  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 RDF and MILE: Why?:  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 RDF Representation of MILE:  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 An RDF Schema for the synt-sem linking:  An RDF Schema for the synt-sem linking <!-- An RDF Schema for ISLE lexical entries v 0.1 2004/05/05 Author: Monachini --> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:owl ="http://www.w3.org/2002/07/owl# xmlns:mlc ="http://www.cs.vassar.edu/~ide/rdf/isle-schema-v.6# xmlns:mlc ="http://www.ilc.cnr.it/clips/rdf/isle-schema-syntsemlinking_v.1#"> <!-- ISLE/MILE lexical objects (classes for the synt-sem linking) --> <rdfs:Class rdf:about="http://www.ilc.cnr.it/clips/rdf/isle-schema-syntsemlinking_v.1#CorrespSynUSemU"> <rdfs:label>CorrespSynUSemU</rdfs:label> <rdfs:comment>This class links a SynU to a SemU</rdfs:comment> </rdfs:Class> <rdfs:Class rdf:about="http://www.ilc.cnr.it/clips/rdf/isle-schema-syntsemlinking_v.1#PredicativeCorresp"> <rdfs:label>PredicativeCorresp</rdfs:label> <rdfs:comment>This class contains the associations between the syntactic slots and semantic argument</rdfs:comment> </rdfs:Class> <rdfs:Class rdf:about="http://www.ilc.cnr.it/clips/rdf/isle-schema-syntsemlinking_v.1#SlotArgCorresp"> <rdfs:label>SlotArgCorresp</rdfs:label> <rdfs:comment>This class links a syntactic slots to a semantic argument</rdfs:comment> </rdfs:Class> Classes An RDF Schema for the synt-sem linking:  An RDF Schema for the synt-sem linking <!-- Properties (relations) between objects and between objects and atomic values --> <rdf:Property rdf:about="http://www.ilc.cnr.it/clips/rdf/isle-schema-syntsemlinking_v.1#hasSourceSynU"> <rdfs:label>hasSourceSynU</rdfs:label> <rdfs:domain rdf:resource="http://www.ilc.cnr.it/clips/rdf/isle-schema-syntsemlinking_v.1#CorrespSynUSemU"/> <rdfs:range rdf:resource="http://www.cs.vassar.edu/~ide/rdf/isle-schema-v.6#SynU"/> </rdf:Property> <rdf:Property rdf:about="http://www.ilc.cnr.it/clips/rdf/isle-schema-syntsemlinking_v.1#hasTargetSemU"> <rdfs:label>hasTargetSemU</rdfs:label> <rdfs:domain rdf:resource="http://www.ilc.cnr.it/clips/rdf/isle-schema-syntsemlinking_v.1#CorrespSynUSemU"/> <rdfs:range rdf:resource="http://www.cs.vassar.edu/~ide/rdf/isle-schema-v.6#SemU"/> </rdf:Property> <rdf:Property rdf:about="http://www.ilc.cnr.it/clips/rdf/isle-schema-syntsemlinking_v.1#hasPredicativeCorresp"> <rdfs:label>hasPredicativeCorresp</rdfs:label> <rdfs:domain rdf:resource="http://www.ilc.cnr.it/clips/rdf/isle-schema-syntsemlinking_v.1#CorrespSynUSemU"/> <rdfs:range rdf:resource="http://www.ilc.cnr.it/clips/rdf/isle-schema-syntsemlinking_v.1#PredicativeCorresp"/> </rdf:Property> <rdf:Property rdf:about="http://www.ilc.cnr.it/clips/rdf/isle-schema-syntsemlinking_v.1#includesSlotArgCorresp"> <rdfs:label>includesSlotArgCorresp</rdfs:label> <rdfs:domain rdf:resource="http://www.ilc.cnr.it/clips/rdf/isle-schema-syntsemlinking_v.1#PredicativeCorresp"/> <rdfs:range rdf:resource="http://www.ilc.cnr.it/clips/rdf/isle-schema-syntsemlinking_v.1#SlotArgCorresp"/> </rdf:Property> Properties The library of Pre-instantiated objects:  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 MDCR for some objects:  MDCR for some objects <!-- Sample LDCR entry for a PredicativeCorresp and SlotArgCorresp objects DataCats for ISLE lexical entries v 0.1 2004/05/17 Author: Monachini --> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" … … <PredicativeCorresp rdf:ID="isobivalent"> <includesSlotArgCorresp rdf:resource=“http://www.ilc.cnr.it/clips/rdf/isle-datacats/SlotArgCorresp#Arg0Slot0 Arg1Slot1“/> </includesSlotArgCorresp> </PredicativeCorresp> <SlotArgCorresp rdf:ID="Arg0Slot0" SlotNumber="0" ArgNumber"0"> </SlotArgCorresp> <SlotArgCorresp rdf:ID="Arg1Slot1" SlotNumber="1" ArgNumber"1"> </SlotArgCorresp> </rdf:RDF> Pre-instantiated PredicativeCorresp Pre-instantiated SlotArgCorresp A Sample Entry in MILE :  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 Sample full entry for amareV:  Sample full entry for amareV <!-- The SynU SemU link --> <correspondsTo> <CorrespSynUSemU> <hasSourceSynU mlcp:ID="SYNUamareV"> </hasSourceSynU> <hasTargetSemU mlcp:ID="SEMUamareEXPEVE"> </hasTargetSemU> <hasPredicativeCorresp> <PredicativeCorresp mlcp:ID="amare-PredCorresp"> <includesSlotArgCorresp> <SlotArgCorresp SlotNumber="0" ArgNumber="0"> </SlotArgCorresp> <SlotArgCorresp SlotNumber="1" ArgNumber="1"> </SlotArgCorresp> </includesSlotArgCorresp> </PredicativeCorresp> </hasPredicativeCorresp> </CorrespSynUSemU> </correspondsTo> </SynU> </hasSynu> The “full” object PredicativeCorresp … the abbreviated entry:  … the abbreviated entry <!-- The SynU SemU link --> <correspondsTo> <CorrespSynUSemU> <hasSourceSynU mlcp:ID="SYNUamareV"> </hasSourceSynU> <hasTargetSemU mlcp:ID="SEMUamareEXPEVE"> </hasTargetSemU> <hasPredicativeCorresp rdf:resource=“http://www.ilc.cnr.it/clips/rdf/isle-datacats/PredicativeCorresp#isobivalent“/> </CorrespSynUSemU> </correspondsTo> </SynU> </hasSynu> Instantiated object PredicativeCorresp Slide107:  The RDF Schema, the DCR for MILE objects and the entries are available at www.ilc.cnr.it/clips/rdf/ and INTERA? …:  and INTERA? … INTERA Multilingual Terminological Lexica will follow and merge the two frameworks The MILE and ISO TMF (Terminological Markup Framework) 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 Beyond MILE: future work Broadening MILE: ... other languages:  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 Using semantically tagged corpora to … acquire semantic info and enhance Lexicons :  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 … Dynamic lexicon:  … 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 What to annotate?:  What to annotate? Mix of: Word-sense annotation (implicit semantic markup) Semantic/conceptual markup … Syntagmatic relations Dependency relations Semantic roles … Need for a common Encoding Policy ?:  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 A few Issues for discussion: MILE & lexicon standards More standardisation initiatives?:  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 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 Broadening MILE: ... other communities Oriented towards open, distributed lexical resources: Lexical Information Servers for multiple access to lexical information repositories A few Issues for discussion: NLP, lexicons, content, ontologies, Semantic Web: … a continuum?:  A few Issues for discussion: NLP, lexicons, content, ontologies, Semantic Web: … a continuum? Need for robust systems, able to acquire/tune multilingual lexical/linguistic/conceptual knowledge, to auto-enrich static basic resources Relation betw. lexical standards & acquisition & text annotation protocols Target….. Multilingual Knowledge Management Technical Feasibility: :  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 To make the Semantic Web a reality ...:  Natural convergence with HLT: multilingual semantic processing ontologies semantic-syntactic computational lexicons To make the Semantic Web a reality ... …need to tackle the twofold challenge of content availability & multilinguality … enables a new role of Multilingual Lexicons: to become essential component for the Semantic Web:  … enables a new role of Multilingual Lexicons: to become essential component for the Semantic Web Language - & lexicons - are the gateway to knowledge Semantic Web developers need repositories of words & terms - & knowledge of their relations in language use & ontological classification The cost of adding this structured and machine-understandable lexical information can be one of the factors that delays its full deployment The effort of making available millions of ‘words’ for dozens of languages is something that no small group is able to afford A radical shift in the lexical paradigm - whereby many participants add linguistic content descriptions in an open distributed lexical framework - required to make the Web usable Beyond MILE: next steps... …. towards an Open Distributed Lexical Infrastucture:  Create a first repository of shared lexical entries “extracted” from different lexical resources & mapped to MILE (choosing e.g. lexical entries in areas related to the Olympic Games) to test mapping different lexicon models to MILE provide a grid with all the ISLE Basic Notions, short descriptions, attributes and sub-elements,to be filled with the correspondent "notions” Create a list (Open Lexicon Interest Group) ... Beyond MILE: next steps... …. towards an Open Distributed Lexical Infrastucture Language Enhance user-adaptivity, resource sharing, cooperative creation & management Lexical Information Servers for multiple access to lexical information repositories Knowledge A new paradigm for a “new generation” of LR?:  A new paradigm for a “new generation” of LR? New Strategic Vision towards a Distributed Open Lexical Infrastructure Focus on cooperation, also between different communities for distributed & cooperative creation, management, etc. of Lexical Resources MILE as a common platform technical & organisational requirements Beyond MILE: towards open & distributed lexicons:  Beyond MILE: towards open & distributed lexicons Semantic Lexicon URI = http://www.xxx… Syntactic Constructions URI = http://www.yyy… Ontology URI = http://www.zzz… Monolingual/Multilingual Lexicon Lex_object: semFeature URI = http://www.xxx…#HUMAN Lex_object: syntagmaNT URI = http://www.zzz…#NP corpora A few issues for the future...:  A few issues for the future... Integration betw. WLR/SLR/MMR (see e.g. LREC) Integration betw. LRs & SemWeb Integration of Lexicons/Terminologies/Ontologies: towards Knowledge Resources Multilingual Resources: an open infrastructure Integration of Lexicon/Corpus (see e.g. Framenet) Parallel evolution of LRs & LTechnology from Computational Lexicons to Knowledge Resources:  from Computational Lexicons to Knowledge Resources Unified framework for lexicons, ontologies, terminologies, etc. Towards an open, distributed infrastructure for lexical resources Lexical Information Servers flexible and extensible integrated with multimodal and multimedial data integrated with Web technology related initiatives: INTERA, ICWLRE …with a world-wide participation looking for an appropriate call:  …with a world-wide participation looking for an appropriate call ….. pushing to launch an Open & Distributed Lexical Infrastructure for content description and content interoperability, to make lexical resources usable within the emerging Semantic Web scenario for Language Resources & Semantic Web…. How to go to a framework allowing incremental creation/merging/…:  How to go to a framework allowing incremental creation/merging/… How to: "organise" creation/acquisition of multilingual LRs: evaluate different models cope with/affect maintenance organise technology transfer among languages support BLARK (a commonly agreed list of minimal requirements for “national” LRs) launch an international initiative linking Semantic Web & LRs bootstrap this by "opening" a few LRs role of standards Lexical WEB & Content Interoperability:  Lexical WEB & Content Interoperability As a critical step for semantic mark-up in the SemWeb MILE with intelligent agents?? A new paradigm for a “new generation” of LRs?:  A new paradigm for a “new generation” of LRs? Cross-lingual links

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