presentation of papers
Maxime Lefrançois, Romain Gugert, Fabien Gandon et al. (2013) Application of the Unit Graphs Framework to Lexicographic Definitions in the RELIEF project. In Proceedings of the 6th International Conference on Meaning-Text Theory (MTT'2013).
and
Maxime Lefrançois, Fabien Gandon (2013) The Unit Graphs Framework: A graph-based Knowledge Representation Formalism designed for the Meaning-Text Theory. In Proceedings of the 6th International Conference on Meaning-Text Theory (MTT'2013).
A general method applicable to the search for anglicisms in russian social ne...Ilia Karpov
In the process of globalization, the number of English words in other languages has rapidly increased. In automatic speech recognition systems, spell-checking, tagging, and other software in the field of natural language processing,
loan words are not easily recognized and should be evaluated
separately. In this paper we present a corpora-based approach to the automatic detection of anglicisms in Russian social network
texts. Proposed method is based on the idea of simultaneous
scripting, phonetics, and semantics similarity of the original Latin word and its Cyrillic analogue. We used a set of transliteration, phonetic transcribing, and morphological analysis methods to find possible hypotheses and distributional semantic models to filter them. Resulting list of borrowings, gathered from approximately 20 million LiveJournal texts, shows good intersection with manually collected dictionary. Proposed method is fully automated and can be applied to any domain–specific area.
Full paper available at:
https://www.academia.edu/29834070/A_General_Method_Applicable_to_the_Search_for_Anglicisms_in_Russian_Social_Network_Texts
A general method applicable to the search for anglicisms in russian social ne...Ilia Karpov
In the process of globalization, the number of English words in other languages has rapidly increased. In automatic speech recognition systems, spell-checking, tagging, and other software in the field of natural language processing,
loan words are not easily recognized and should be evaluated
separately. In this paper we present a corpora-based approach to the automatic detection of anglicisms in Russian social network
texts. Proposed method is based on the idea of simultaneous
scripting, phonetics, and semantics similarity of the original Latin word and its Cyrillic analogue. We used a set of transliteration, phonetic transcribing, and morphological analysis methods to find possible hypotheses and distributional semantic models to filter them. Resulting list of borrowings, gathered from approximately 20 million LiveJournal texts, shows good intersection with manually collected dictionary. Proposed method is fully automated and can be applied to any domain–specific area.
Full paper available at:
https://www.academia.edu/29834070/A_General_Method_Applicable_to_the_Search_for_Anglicisms_in_Russian_Social_Network_Texts
Language variety identification is an author profiling subtask which aims to detect lexical and semantic variations in order to classify different varieties of the same language. In this work we approach the task by using distributed representations based on Mikolov et al. investigations.
Language variety identification aims at labelling texts in a native lan- guage (e.g. Spanish, Portuguese, English) with its specific variation (e.g. Ar- gentina, Chile, Mexico, Peru, Spain; Brazil, Portugal; UK, US). In this work we propose a low dimensionality representation (LDR) to address this task with five different varieties of Spanish: Argentina, Chile, Mexico, Peru and Spain. We compare our LDR method with common state-of-the-art representations and show an increase in accuracy of ∼35%. Furthermore, we compare LDR with two reference distributed representation models. Experimental results show com- petitive performance while dramatically reducing the dimensionality — and in- creasing the big data suitability — to only 6 features per variety. Additionally, we analyse the behaviour of the employed machine learning algorithms and the most discriminating features. Finally, we employ an alternative dataset to test the robustness of our low dimensionality representation with another set of similar languages.
Some issues of contention in contrastive analysisSoraya Ghoddousi
This is a summarise of chapter 7 of Contrastive Analysis book by Carl James in Some Issues of Contention as a Midterm Project of CA course in TEFL at PAYAM NOOR University (Distance Education)
Visual-Semantic Embeddings: some thoughts on LanguageRoelof Pieters
Language technology is rapidly evolving. A resurgence in the use of distributed semantic representations and word embeddings, combined with the rise of deep neural networks has led to new approaches and new state of the art results in many natural language processing tasks. One such exciting - and most recent - trend can be seen in multimodal approaches fusing techniques and models of natural language processing (NLP) with that of computer vision.
The talk is aimed at giving an overview of the NLP part of this trend. It will start with giving a short overview of the challenges in creating deep networks for language, as well as what makes for a “good” language models, and the specific requirements of semantic word spaces for multi-modal embeddings.
Language variety identification is an author profiling subtask which aims to detect lexical and semantic variations in order to classify different varieties of the same language. In this work we approach the task by using distributed representations based on Mikolov et al. investigations.
Language variety identification aims at labelling texts in a native lan- guage (e.g. Spanish, Portuguese, English) with its specific variation (e.g. Ar- gentina, Chile, Mexico, Peru, Spain; Brazil, Portugal; UK, US). In this work we propose a low dimensionality representation (LDR) to address this task with five different varieties of Spanish: Argentina, Chile, Mexico, Peru and Spain. We compare our LDR method with common state-of-the-art representations and show an increase in accuracy of ∼35%. Furthermore, we compare LDR with two reference distributed representation models. Experimental results show com- petitive performance while dramatically reducing the dimensionality — and in- creasing the big data suitability — to only 6 features per variety. Additionally, we analyse the behaviour of the employed machine learning algorithms and the most discriminating features. Finally, we employ an alternative dataset to test the robustness of our low dimensionality representation with another set of similar languages.
Some issues of contention in contrastive analysisSoraya Ghoddousi
This is a summarise of chapter 7 of Contrastive Analysis book by Carl James in Some Issues of Contention as a Midterm Project of CA course in TEFL at PAYAM NOOR University (Distance Education)
Visual-Semantic Embeddings: some thoughts on LanguageRoelof Pieters
Language technology is rapidly evolving. A resurgence in the use of distributed semantic representations and word embeddings, combined with the rise of deep neural networks has led to new approaches and new state of the art results in many natural language processing tasks. One such exciting - and most recent - trend can be seen in multimodal approaches fusing techniques and models of natural language processing (NLP) with that of computer vision.
The talk is aimed at giving an overview of the NLP part of this trend. It will start with giving a short overview of the challenges in creating deep networks for language, as well as what makes for a “good” language models, and the specific requirements of semantic word spaces for multi-modal embeddings.
Formal and Computational Representations
The Semantics of First-Order Logic
Event Representations
Description Logics & the Web Ontology Language
Compositionality
Lamba calculus
Corpus-based approaches:
Latent Semantic Analysis
Topic models
Distributional Semantics
Automatic Grammatical Error Correction for ESL-Learners by SMT - Getting it r...Marcin Junczys-Dowmunt
Video recording of full talk: http://lectures.ms.mff.cuni.cz/view.php?rec=255
There has been an increasing interest in using statistical machine translation (SMT) for the task of Grammatical Error Correction (GEC) for English-as-a-Second-Language (ESL) learners. Two of the three highest-scoring systems of the CoNLL-2014 Shared Task were SMT-based. The currently highest-scoring result published for the CoNLL-2014 test set has been achieved by a system combination of the five best CoNLL-2014 submissions built with MEMT (a tool for MT system combination). In this talk, we demonstrate how a single SMT-based system can match and outperform the result of the mentioned combined system. Furthermore, this system outperforms any other published results (including our own CoNLL-2014 submission) for a single system by a margin of several percent F-Score when the same training data is being used. These results are achieved by adapting current state-of-the art methods for phrase-based SMT specifically to the problem of GEC.
We report on the effects of:
- Parameter tuning for GEC
- Introducing GEC-specific dense and sparse features
- Using large-scale data
Tooltip-type, Frame-type, and Concordance Glossing in L2 Readingengedukamall
Lee, J. (2014, September). Tooltip-type, frame-type, and concordance glossing in L2 reading. Paper presented at the meeting of KAMALL Annual Conference 2014, Seoul, Korea.
[Abstract]
This study investigated the effects of three different types of electronic textual
glossing, namely tooltip-type, frame-type, and concordance glossing, on
foreign language (FL) vocabulary acquisition. The present study was primarily
driven by Nation’s (2009) introduction to the different types of glossing
available for enhancing FL vocabulary learning in computer-assisted learning
environments, and his suggestion that these glossing types be compared in
terms of their effectiveness. While the first two glossing types both provide
the definitions of glossed words but are different from each other in terms of
their user interface designs. In the case of tooltip-type glossing, a pop-up box
showing the definition of a glossed word temporarily appears when a reader
hovers the mouse cursor over the glossed word, and it disappears when he
or she moves the cursor away from the word. This glossing format is
designed in such a way that it would not obscure any surrounding contexts
around the glossed word. On the other hand, in the frame-type glossing, the
definition appears in the bottom frame of the screen when a reader clicks the
glossed word. In the concordance glossing, the glossing device is equipped
with concordance sentences involving the glossed words, through which a
reader is given three authentic sentences from two authoritative reference
corpora (“British National Corpus” and “Brown”) in the frame-type format. A
total of 83 university students of English as a Foreign Language (EFL)
participated in the study. They completed a computer-based reading task, a
reading comprehension test, meaning recall vocabulary tests at three different
points in time, and a post-reading questionnaire. Our findings showed that
the intermediate EFL learners were affected not by a difference in terms of
glossing formats, but by the type of information provided, with tooltip-type
and frame-type glossing bringing about more positive outcomes in terms of
vocabulary learning. On the other hand, these glossing types were found to make no difference in terms of students’ reading comprehension. The findings
further revealed that the tooltip-type and frame-type groups made greater
gains of target vocabulary, while the three groups all experienced a similar
amount of cognitive load, and that these groups consequently rated their
respective glossing more positively than the concordance group.
ISOcat and RELcat, two cooperating semantic registriesMenzo Windhouwer
M. Windhouwer, I. Schuurman. ISOcat and RELcat, two cooperating semantic registries. At the 24th Meeting of Computational Linguistics in the Netherlands (CLIN 24), Leiden, The Netherlands, January 17, 2014.
In this talk I intend to review some basic and high-level concepts like formal languages, grammars and ontologies. Languages to transmit knowledge from a sender to a receiver; grammars to formally specify languages; ontologies as formals specifications of specific knowledge domains. After this introductory revision, enhancing the role of each of those elements in the context of computer-based problem solving (programming), I will talk about a project aimed at automatically infer and generate a Grammar for a Domain Specific Language (DSL) from a given ontology that describes this specific domain. The transformation rules will be presented and the system, Onto2Gra, that fully implements that "Ontological approach for DSL development" will be introduced.
SPARQL-Generate - query and generate both RDF and text
Generate RDF or text from web documents in XML, JSON, CSV, GeoJSON, HTML, CBOR, plain text with regular expressions.
Generate RDF or text streams from large CSV documents, MQTT or WebSocket streams, repeated HTTP Get operations.
Directly generate HDT as the output of big transformations.
Use it as partial STTL and SPARQL Function implementations.
Extend it to support new data sources and formats.
SPARQL-Generate is an extension of SPARQL 1.1 for querying not only RDF datasets but also documents in arbitrary formats.
It offers a simple and expressive template-based option to generate RDF Graphs or text, from documents and different streams. It presents the following advantages:
Anyone familiar with SPARQL can easily learn SPARQL-Generate;
Learning SPARQL-Generate helps you learning SPARQL;
SPARQL-Generate leverages the expressivity of SPARQL 1.1: Aggregates, Solution Sequences and Modifiers, SPARQL functions and their extension mechanism.
It integrates seamlessly with existing standards for consuming Semantic Web data, such as SPARQL or Semantic Web programming frameworks.
Presentation made for the event "Digital transformation in France and Germany: Consequences for industry, society & higher education" organized by the French-German University in cooperation with Institut Mines-Télécom https://www.dfh-ufa.org/fr/digital-transformation-in-france-and-germany/
Overview of the SPARQL-Generate language and latest developmentsMaxime Lefrançois
SPARQL-Generate is an extension of SPARQL 1.1 for querying not only RDF datasets but also documents in arbitrary formats. The solution bindings can then be used to output RDF (SPARQL-Generate) or text (SPARQL-Template)
Anyone familiar with SPARQL can easily learn SPARQL-Generate; Learning SPARQL-Generate helps you learning SPARQL.
The open-source implementation (Apache 2 license) is based on Apache Jena and can be used to execute transformations from a combination of RDF and any kind of documents in XML, JSON, CSV, HTML, GeoJSON, CBOR, streams of messages using WebSocket or MQTT... (easily extensible)
Recent extensions and improvement include:
- heavy refactoring to support parallelization
- more expressive iterators and functions
- simple generation of RDF lists
- support of aggregates
- generation of HDT (thanks Ana for the use case)
- partial implementation of STTL for the generation of Text (https://ns.inria.fr/sparql-template/)
- partial implementation of LDScript (http://ns.inria.fr/sparql-extension/)
- integration of all these types of rules to decouple or compose queries, e.g.:
- call a SPARQL-Generate query in the SPARQL FROM clause
- plug a SPARQL-Generate or a SPARQL-Template query to the output of a SPARQL-
Select function
- a Sublime Text package for local development
Ph.D. Defense: Représentation des connaissances sémantiques lexicales de la T...Maxime Lefrançois
slides of Ph.D. defense:
"Meaning-Text Theory Lexical Semantic Knowledge Representation : Conceptualization, Representation, and Operationalization of Lexicographic Definitions"
Maxime Lefrançois (2014) Représentation des connaissances sémantiques lexicales de la Théorie Sens-Texte : Conceptualisation, représentation, et opérationnalisation des définitions lexicographiques.
Représentation des connaissances du DEC: Concepts fondamentaux du formalisme ...Maxime Lefrançois
Présenté lors de la conférence RECITAL'13,
http://maxime-lefrancois.info/Publications
Dans cet article nous nous intéressons au choix d'un formalisme de représentation des connaissances qui nous permette de représenter, manipuler, interroger et raisonner sur des connaissances linguistiques du Dictionnaire Explicatif et Combinatoire (DEC) de la Théorie Sens-Texte. Nous montrons que ni les formalismes du web sémantique ni le formalisme des Graphes conceptuels n'est adapté pour cela, et justifions l'introduction d'un nouveau formalisme dit des Graphes d'Unités. Nous introduisons la hiérarchie des Types d'Unités au cœur du formalisme, et présentons les Graphes d'Unités ainsi que la manière dont on peut les utiliser pour représenter certains aspects du DEC.
ECD Knowledge Representation: Fundamental Concepts of the Unit Graphs Framework
In this paper we are interested in the choice of a knowledge representation formalism that enables the representation, manipulation, query, and reasoning over linguistic knowledge of the Explanatory and Combinatorial Dictionary (ECD) of the Meaning-Text Theory. We show that neither the semantic web formalisms nor the Conceptual Graphs Formalism suit our needs, and justify the introduction of a new formalism denoted Unit Graphs. We introduce the core of this formalism which is the Unit Types hierarchy, and present Unit Graphs and how one may use them to represent aspects of the ECD.
Slides of the second paper on the ULiS project, availiable at http://maxime-lefrancois.info/Publications
We are interested in bridging the world of natural language and the world of the semantic web in particular to support multilingual access to the web of data. In this paper we introduce the ULiS project, that aims at designing a pivot-based NLP technique called Universal Linguistic System, 100% using the semantic web formalisms, and being compliant with the Meaning-Text theory. Through the ULiS, a user could interact with an interlingual knowledge base (IKB) in controlled natural language. Linguistic resources themselves are part of a specific IKB: The Universal Lexical Knowledge base (ULK), so that actors may enhance their controlled natural language, through requests in controlled natural language. We describe a basic interaction scenario at the system level, and provide an overview of the architecture of ULiS. We then introduce the core of the ULiS: the interlingual lexical ontology (ILexicOn), in which each interlingual lexical unit class (ILUc) supports the projection of its semantic decomposition on itself. We validate our model with a standalone ILexicOn, and introduce and explain a concise human-readable notation for it.
Slides of the first paper on the ULiS project, availiable at http://maxime-lefrancois.info/Publications
We are interested in bridging the world of natural language and the world of the semantic web in particular to support natural multilingual access to the web of data. In this paper we introduce a new type of lexical ontology called interlingual lexical ontology (ILexicOn), which uses semantic web formalisms to make each interlingual lexical unit class (ILUc) support the projection of its semantic decomposition on itself. After a short overview of existing lexical ontologies, we briefly introduce the semantic web formalisms we use. We then present the three layered architecture of our approach: i) the interlingual lexical metaontology (ILexiMOn); ii) the ILexicOn where ILUcs are formally defined; iii) the data layer. We illustrate our approach with a standalone ILexicOn, and introduce and explain a concise human-readable notation to represent ILexicOns. Finally, we show how semantic web formalisms enable the projection of a semantic decomposition on the decomposed ILUc.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
Thesis Statement for students diagnonsed withADHD.ppt
MTT-2013
1. The Unit Graphs Framework: A graph-based
Knowledge Representation Formalism designed
for the Meaning-Text Theory
&
Application to Lexicographic Definitions in the
RELIEF project
Maxime Lefrançois, Fabien Gandon
[ maxime.lefrancois | fabien.gandon ] @inria.fr
MTT’13 -1, August 30th 2013, Prague
2. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation 2
Knowledge Representation
• answers recurrent needs
▫ represent
▫ manipulate
▫ query
▫ reason
▫ share
▫ ...
• here: applied to the linguistic domain
▫ Meaning-Text Theory
3. 3
Choose Formalism
Populate
Applications
t
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
4. 4
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Choose Formalism
Populate
Applications
t
1. Choose Formalism
5. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
1. Choose Formalism
5
• Needs, problems
• Semantic Web ?
▫ false good idea
• Conceptual Graphs ?
▫ nop
• The Unit Graphs formalism
▫ Hierarchy of Unit Types
• A deep representation level for meanings
• Application to Lexicographic Definitions in RELIEF
▫ Scenario: Actantial Structure
▫ Unit Graphs and PUT Definitions
▫ Scenario: DSemUT formal Definition
▫ Scenario: Deep-Surface Correspondence
6. 6
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Meaning-Text Theory
1932
1893 54 59
65
88 96
04
L. Tesnière
I.A. Mel’cuk 91
65 – Begining
88 – Dependency Grammar
91 – Introduction à la Lexicologie Explicative et Combinatoire
96 – Lexical Functions
04 – Actants in Semantics and Syntax
7. 7
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Meaning-Text Theory
1932
1893 54 59
65
88 96
04
L. Tesnière
I.A. Mel’cuk 91
65 – Begining
88 – Dependency Grammar
91 – Introduction à la Lexicologie Explicative et Combinatoire
96 – Lexical Functions
04 – Actants in Semantics and Syntax
8. 8
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Need 1: Lexicographic Definitions in
RELIEF
(Polguère, 2009; Lux-Pogodalla & Polguère, 2011)
PEIGNE2a :
(Weaving tool that a person X uses to untangle#2 fibres of an object Y)
~ of a person X for object Y
<CC label="tool">weaving tool</CC>
<PC role="use">that X uses to untangle#2 fibres of Y</PC>
(Barque & Polguère, 2008)
9. 9
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Need 1: Lexicographic Definitions in
RELIEF (Polguère, 2009; Lux-Pogodalla & Polguère, 2011)
PEIGNE2a :
(Weaving tool that a person X uses to untangle#2 fibres of an object Y)
~ of a person X for object Y
<CC label="tool">weaving tool</CC>
<PC role="use">that X uses to untangle#2 fibres of Y</PC>
TOOL
~ of person X for activity Y
PEIGNER2 :
person X ~ fibres Y
(Barque & Polguère, 2008)
10. 10
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Need 1: Lexicographic Definitions in
RELIEF
(Polguère, 2009; Lux-Pogodalla & Polguère, 2011)
PEIGNE2a :
(Weaving tool that a person X uses to untangle#2 fibres of an object Y)
~ of a person X for object Y
<CC label="tool">weaving tool</CC>
<PC role="use">that X uses to untangle#2 fibres of Y</PC>
TOOL
~ of person X for activity Y
PEIGNER2 :
person X ~ fibres Y
(Barque and Polguère, 2008)
11. 11
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Need 1: Lexicographic Definitions in
RELIEF
(Polguère, 2009; Lux-Pogodalla & Polguère, 2011)
PEIGNE2a :
(Weaving tool that a person X uses to untangle#2 fibres of an object Y)
~ of a person X for object Y
<CC label="tool">weaving tool</CC>
<PC role="use">that X uses to untangle#2 fibres of Y</PC>
TOOL
~ of person X for activity Y
PEIGNER2 :
person X ~ fibres Y
(Barque and Polguère, 2008)
Formalization level not reached today
12. 12
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Meaning-Text Theory
1932
1893 54 59
65
88 96
04
L. Tesnière
I.A. Mel’cuk 91
65 – Begining
88 – Dependency Grammar
91 – Introduction à la Lexicologie Explicative et Combinatoire
96 – Lexical Functions
04 – Actants in Semantics and Syntax
13. 13
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Need 2: Theory of Semantic Actants
Linguistic Predicates
Semantic Actant Slots (SemASlots)
(Mel’cuk, 2004)
= Participants of the linguistic situation denoted by L
that have a favoured position in sentences constructed with L
14. 14
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Need 2: Theory of Semantic Actants
Linguistic Predicates
Semantic Actant Slots (SemASlots)
(Mel’cuk, 2004)
= Participants of the linguistic situation denoted by L
that have a favoured position in sentences constructed with L
15. 15
Need 2: Theory of Semantic Actants
Linguistic Predicates
(Mel’cuk, 2004)
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
optional
logical
predicate ≠
(to eat)(Paul ; eggs ; plate)
16. 16
Need 2: Theory of Semantic Actants
Linguistic Predicates
(Mel’cuk, 2004)
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
optional
(to eat)(Paul ; eggs ; plate)
(outil)(Paul ; Untangle)
(outil)(Paul ; Carpenter)
split
logical
predicate ≠
17. 17
Need 2: Theory of Semantic Actants
Linguistic Predicates
(Mel’cuk, 2004)
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
optional
logical
predicate ≠
(to eat)(Paul ; eggs ; plate)
(outil)(Paul ; Untangle)
(outil)(Paul ; Carpenter)
split
an actant may be a predicate
18. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
1. Choose Formalism
18
• Needs, problems
• Semantic Web ?
▫ false good idea
• Conceptual Graphs ?
▫ nop
• The Unit Graphs formalism
▫ Hierarchy of Unit Types
• A deep representation level for meanings
• Application to Lexicographic Definitions in RELIEF
▫ Scenario: Actantial Structure
▫ Unit Graphs and PUT Definitions
▫ Scenario: DSemUT formal Definition
▫ Scenario: Deep-Surface Correspondence
19. 19
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Semantic Web
1932
1893 54 59
65
88 96
04
L. Tesnière
I.A. Mel’cuk 91
RDF: oriented labelled graphs.
standard for the representation and exchange
of structured knowledge
OWL: Description Logics
SPARQL: Query, ...
20. 20
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Semantic Web
1932
1893 54 59
65
88 96
04
L. Tesnière
I.A. Mel’cuk 91
Problems in word word
• RDF: not enough logical semantics
OK as syntax for knowledge exchange
21. 21
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Semantic Web
1932
1893 54 59
65
88 96
04
L. Tesnière
I.A. Mel’cuk 91
Problems in word word
• OWL: only binary relations
reify ? -> no logical semantics
22. 22
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
ULiS project: represent lexicographic definitions with OWL
(Lefrançois & Gandon, MTT’2011, TIA’2011, MSW’2011)
Semantic Web
23. 23
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Semantic Web
1893 54 59
Problems in word word
ULiS
1932
65
88 96
04
L. Tesnière
I.A. Mel’cuk 91
need OWL full + rules (undecidable)
24. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
1. Choose Formalism
24
• Needs, problems
• Semantic Web ?
▫ false good idea
• Conceptual Graphs ?
▫ nop
• The Unit Graphs formalism
▫ Hierarchy of Unit Types
• A deep representation level for meanings
• Application to Lexicographic Definitions in RELIEF
▫ Scenario: Actantial Structure
▫ Unit Graphs and PUT Definitions
▫ Scenario: DSemUT formal Definition
▫ Scenario: Deep-Surface Correspondence
25. 25
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Conceptual Graphs
1932
1893 54 59
65
88 96
04
L. Tesnière
I.A. Mel’cuk 91
1940
84
J.F. Sowa
• Oriented labelled Graphs
• Sowa drew his inspiration from Tesnière
• Rules, reasoning, (Baget, Mugnier, Chein, ...)
• Concepts and Relations definitions, (Sowa, Leclère, ...)
26. 26
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Conceptual Graphs
1932
1893 54 59
65
88 96
04
L. Tesnière
I.A. Mel’cuk 91
1940
84
J.F. Sowa
Problems in word word
• Alternation concept-relation
27. 27
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Conceptual Graphs
1932
1893 54 59
65
88 96
04
L. Tesnière
I.A. Mel’cuk 91
1940
84
J.F. Sowa
Problems in word word
• Alternation concept-relation
• reify ? -> no logical semantics
28. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
1. Choose Formalism
28
• Needs, problems
• Semantic Web ?
▫ false good idea
• Conceptual Graphs ?
▫ nop
• The Unit Graphs formalism
▫ Hierarchy of Unit Types
• A deep representation level for meanings
• Application to Lexicographic Definitions in RELIEF
▫ Scenario: Actantial Structure
▫ Unit Graphs and PUT Definitions
▫ Scenario: DSemUT formal Definition
▫ Scenario: Deep-Surface Correspondence
29. 29
t
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Choose Formalism
Populate
The Unit Graphs Formalism
• a graph-based formalism,
• to represent linguistic units
Applications
30. 30
t
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Choose Formalism
Populate
The Unit Graphs Formalism
Draw inspiration from GC
and
Develop a RDF syntax
to exchange knowledge
Applications
31. 31
t
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Choose Formalism
Populate
The Unit Graphs Formalism
Applications
Draw inspiration from GC
and
Develop a RDF syntax
to exchange knowledge
32. 32
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Units – Representations
(c.f., Mel’čuk, 2004)
Unit Types – Lexicon
33. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
1. Choose Formalism
33
• Needs, problems
• Semantic Web ?
▫ false good idea
• Conceptual Graphs ?
▫ nop
• The Unit Graphs formalism
▫ Hierarchy of Unit Types
• A deep representation level for meanings
• Application to Lexicographic Definitions in RELIEF
▫ Scenario: Actantial Structure
▫ Unit Graphs and PUT Definitions
▫ Scenario: DSemUT formal Definition
▫ Scenario: Deep-Surface Correspondence
34. 34
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Surface
Semantics
Deep
Syntax
Surface
Syntax
Texts
Linguistic Unit Types
35. 35
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Surface
Semantics
Deep
Syntax
Surface
Syntax
Texts
Linguistic Unit Types
• have an Actantial structure
▫ Optional, obligatory, prohibited Actant Slots (ASlots)
▫ have a signature
It specifies how their instances
shall be linked to other units in UGs
36. 36
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Surface
Semantics
Deep
Syntax
Surface
Syntax
Texts
Linguistic Unit Types
• have an Actantial structure
▫ Optional, obligatory, prohibited Actant Slots (ASlots)
▫ have a signature
It specifies how their instances
shall be linked to other units in UGs
• are described in a hierarchy
▫ a Unit Type inherits and may specialize
the Actantial Structure of its parents
37. 37
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Hierarchy of Unit Types
Actant Symbols (ASymbols)
Surface Semantics: Numbers
Deep Syntax: Roman numerals
...
38. 38
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Hierarchy of Unit Types
Primitive Unit Types (PUTs)
is the disjoint union of:
the set of declared PUTs
ex: Lexical unit type ANIMAL
Grammatical unit type Verb, Noun, plur
Surface Semantic unit type (animal)
radices (the roots)
obligant (those that make obligatory)
prohibent (those that prohibit)
The prime absurd PUT
The prime universal PUTs
39. 39
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Hierarchy of Unit Types
The Actantial Structure of Unit Types
is the set of its obligatory, prohibited,
and optional ASlots, and their signature
40. 40
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Hierarchy of Unit Types
Pre-order over PUTs
is used to compute a pre-order over PUTs,
and to assign a set of ASlots to each PUT.
t has an ASlot s iif t is a descendent of γ(s) s ϵ α(t)
Aslot s is obligatory iif t is a descendent of γ1(s) s ϵ α1(t)
ASlot s is prohibited iif t is a descendent of γ0(s) s ϵ α0(t)
ASlot s is optional iif t is neither obligatory nor prohibited s ϵ α? (t)
ex: X eats Y (in Z) : (γ1(1), (eat)), (γ1(2), (eat)), (γ(3), (eat))
(γ(subject), Verb)
(pluralizable, plur)
((animal) ,(dog)) ?
41. 41
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Hierarchy of Unit Types
Signatures of ASlots
denotes the type of units
that fill ASlot s of a unit of type t
ex:
42. 42
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Hierarchy of Unit Types
A Unit may consist of several conjoint PUTs
Conjunctive Unit Types (CUTs)
ex: { def, plur, ANIMAL } ((the animals))
the actantial structure of PUTs
is naturally extended to CUTs
some CUTs are asserted to be absurd.
the pre-order over PUTs
is extended to a pre-order over CUTs
absurd CUTs are those lower than
the prime absurd PUT
43. 43
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
≃
radix
obligat
prohibet
absurd
Organization of the Unit Types Hierarchy
with respect to a unique ASymbol s
The complete Unit Types Hierarchy
is an intricated superposition of such figures
44. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
1. Choose Formalism
44
• Needs, problems
• Semantic Web ?
▫ false good idea
• Conceptual Graphs ?
▫ nop
• The Unit Graphs formalism
▫ Hierarchy of Unit Types
• A deep representation level for meanings
• Application to Lexicographic Definitions in RELIEF
▫ Scenario: Actantial Structure
▫ Unit Graphs and PUT Definitions
▫ Scenario: DSemUT formal Definition
▫ Scenario: Deep-Surface Correspondence
45. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation 45
Hierarchy of Meanings ?
hierarchy of UT
?
=
hierarchy of meanings
46. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation 46
Hierarchy of Meanings ?
hierarchy of UT
?
=
hierarchy of meanings
• (outil) (tool)
• ASlot 1 – person that uses the tool
• ASlot 2 – either activity or profession
• (ciseaux) (scissors)
• ASlot 1 – person that uses the scissors
• ASlot 2 – the object to be cut
47. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation 47
Hierarchy of Meanings ?
• (outil) (tool)
Is an activity or a profession
a kind of object ?
- NO !
• ASlot 1 – person that uses the tool
• ASlot 2 – either activity or profession
• (ciseaux) (scissors)
• ASlot 1 – person that uses the scissors
• ASlot 2 – the object to be cut
48. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation 48
Hierarchy of Meanings ?
• We introduce a deeper level of representation:
The Deep Semantic Level
• notation /outil
• ASymbols are Lexicalized semantic roles
hierarchy of DSemUT
=
hierarchy of meanings
49. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation 49
Deep Semantic Unit Types
• ASlots of /L correspond to:
Obligatory or optional participants of SIT(L)
that are:
• SemASlots of L
• or SemASlots of a L’ such that /L’ < /L
50. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation 50
Deep Semantic Unit Types
Example of inheritance and specialization
in the hierarchy of Deep Semantic Unit Types
51. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
1. Choose Formalism
51
• Needs, problems
• Semantic Web ?
▫ false good idea
• Conceptual Graphs ?
▫ nop
• The Unit Graphs formalism
▫ Hierarchy of Unit Types
• A deep representation level for meanings
• Application to Lexicographic Definitions in RELIEF
▫ Scenario: Actantial Structure
▫ Unit Graphs and PUT Definitions
▫ Scenario: DSemUT formal Definition
▫ Scenario: Deep-Surface Correspondence
52. 52
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Need 1: Lexicographic Definitions in
RELIEF (Polguère, 2009; Lux-Pogodalla & Polguère, 2011)
PEIGNE2a :
(Weaving tool that a person X uses to untangle#2 fibres of an object Y)
~ of a person X for object Y
<CC label="tool">weaving tool</CC>
<PC role="use">that X uses to untangle#2 fibres of Y</PC>
TOOL
~ of person X for activity Y
PEIGNER2 :
person X ~ fibres Y
(Barque & Polguère, 2008)
53. 53
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Need 1: Lexicographic Definitions in
RELIEF
(Polguère, 2009; Lux-Pogodalla & Polguère, 2011)
PEIGNE2a :
(Weaving tool that a person X uses to untangle#2 fibres of an object Y)
~ of a person X for object Y
<CC label="tool">weaving tool</CC>
<PC role="use">that X uses to untangle#2 fibres of Y</PC>
TOOL
~ of person X for activity Y
PEIGNER2 :
person X ~ fibres Y
(Barque and Polguère, 2008)
54. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
1. Choose Formalism
54
• Needs, problems
• Semantic Web ?
▫ false good idea
• Conceptual Graphs ?
▫ nop
• The Unit Graphs formalism
▫ Hierarchy of Unit Types
• A deep representation level for meanings
• Application to Lexicographic Definitions in RELIEF
▫ Scenario: Actantial Structure
▫ Unit Graphs and PUT Definitions
▫ Scenario: DSemUT formal Definition
▫ Scenario: Deep-Surface Correspondence
55. 55
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Definition 1: Actantial Structure
56. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
1. Choose Formalism
56
• Needs, problems
• Semantic Web ?
▫ false good idea
• Conceptual Graphs ?
▫ nop
• The Unit Graphs formalism
▫ Hierarchy of Unit Types
• A deep representation level for meanings
• Application to Lexicographic Definitions in RELIEF
▫ Scenario: Actantial Structure
▫ Unit Graphs and PUT Definitions
▫ Scenario: DSemUT formal Definition
▫ Scenario: Deep-Surface Correspondence
57. 57
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Unit Graphs
are defined over a Support
Unit Node Markers
Arbitrary Symbols
Every Element of M identifies a specific unit;
Multiple elements of M may identify the same unit.
58. 58
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Unit Graphs
are defined over a Support
Unit nodes
Unit nodes labels : a type + a marker
Actantial triples
Circumstantial triples
Declared equivalences of unit nodes
59. 59
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
PUT Definition
The Lexicographic definition of L
corresponds to the definition of /L
60. 60
Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
PUT Definition
The Lexicographic definition of L
corresponds to the definition of /L
• An equivalence between two Unit Graphs
61. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
1. Choose Formalism
61
• Needs, problems
• Semantic Web ?
▫ false good idea
• Conceptual Graphs ?
▫ nop
• The Unit Graphs formalism
▫ Hierarchy of Unit Types
• A deep representation level for meanings
• Application to Lexicographic Definitions in RELIEF
▫ Scenario: Actantial Structure
▫ Unit Graphs and PUT Definitions
▫ Scenario: DSemUT formal Definition
▫ Scenario: Deep-Surface Correspondence
62. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation 62
Definition 2: DSemUT Definition
63. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation 63
Definition 2: DSemUT Definition
64. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation 64
Definition 2: DSemUT Definition
65. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation 65
Definition 2: DSemUT Definition
66. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation 66
Definition 2: DSemUT Definition
67. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation 67
Definition 2: DSemUT Definition
68. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
1. Choose Formalism
68
• Needs, problems
• Semantic Web ?
▫ false good idea
• Conceptual Graphs ?
▫ nop
• The Unit Graphs formalism
▫ Hierarchy of Unit Types
• A deep representation level for meanings
• Application to Lexicographic Definitions in RELIEF
▫ Scenario: Actantial Structure
▫ Unit Graphs and PUT Definitions
▫ Scenario: DSemUT formal Definition
▫ Scenario: Deep-Surface Correspondence
69. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation 69
Definition 3:
Deep – Surface correspondence
70. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
• Design of a prototype web application to represent
formal lexicographic definitions using UGs
• Demonstration online (in French) Edition of the
lexicographic definition of lexical unit PEIGNE2a
▫ wimmics.inria.fr/doc/video/UnitGraphs/editor1.html
• Validation with the RELIEF lexicographers
70
Implementation / Validation
71. Lefrançois, Gandon, The Unit Graphs Framework: Linguistic Knowledge Representation
Conclusions
71
• Linguistic Knowledge Representation
• 1. Choose Formalims
• Identify limitations of existing formalisms
• Suppress these limitations with the Unit Graphs
▫ Hierarchy of Unit Types
▫ Need 2: Theory of semantic actants
▫ Need 1: Lexicographic Definitions in the RLF
72. MTT’13 , August 30th 2013, Prague
The Unit Graphs Framework: A graph-based
Knowledge Representation Formalism designed
for the Meaning-Text Theory
&
Application to Lexicographic Definitions in the
RELIEF project
Thank you
Editor's Notes
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax
Tesnière, 1893-1954, U.Montpellier, FR,
Elements de syntaxe structurale, 1959
Chomsky, 1928- MIT, US,
1950: grammaire générative et transformationnelle, linguistique générative
- Distinction compétence performance - Structure de surface et structure profonde
- Règles de réécriture pour générer – analyser le langage
Cherche une grammaire universelle, avec des universaux et des variables
Sowa 1940
1984 Conceptual Structures - Information Processing in Mind and Machine.
Mel’cuk
1974 développement de la théorie sens-texte
1988 dependency syntax