1. LTeL - Language Technology for eLearning -
Paola Monachesi, Lothar Lemnitzer, Kiril Simov, Dan Cristea,Alex Killing, Diane Evans, Cristina Vertan

2. LT4eL - Language Technology for eLearning
Start date: 1 December 2005
Duration: 30 months
Partners: 12
EU finacing: 1.5 milion Euro
Type project: STREP IST-4

3. LT4eL - Partners Utrecht University, The Netherlands (coordinator)
University of Hamburg, Germany
University “Al.I.Cuza” of Iasi, Romania
University of Lisbon, Portugal
Charles University Prague, Czech Republic
IPP, Bulgarian Academy of Sciences, Bulgaria
University of Tübingen, Germany
ICS, Polish Academy of Sciences, Poland
Zürich University of Applied Sciences Winterthur, Switzerland
University of Malta, Malta
Eidgenössische Hochschule Zürich
Open University, United Kingdom

4. LT4eL -Aims Improve retrieval of learning material
Facilitate construction of user specific courses
Improve creation of personalized content
Support decentralization of content management
Allow for multilingual retrieval of content

5. LT4eL - Languages Bulgarian Czech Dutch German Maltese Polish
Portuguese
Romanian
English

6. LT4eL- Objectives -1- Scientific and Technological Objectives
Creation of an archive of learning objects and linguistic resources
Integration of language technology resources and tools in eLearning
Integration of semantic Knowledge in eLearning
Integration of functionalities in open source LMS
Validation of enhanced LMS

7. LT4eL- Objectives -2- Political objectives Support multilinguality
Knowledge transfer
Awareness raising
Exploitation of resources
Facilitate access to education

8. Tasks Creation of an archive of learning objects
Semi-automatic metadata generation driven by Language Technology resources and NLP tools;
Enhancing eLearning with semantic knowledge;
Integration of the new functionalities in the ILIAS Learning Management System;
Validation of new functionalities in the ILIAS Learning Management System;
Address Multilinguality

9. LMS User Profile LING. PROCESSOR CROSSLINGUAL RETRIEVAL CONVERTOR 2
Documents SCORM
Pseudo-Struct.
Basic XML
LING. PROCESSOR
Lemmatizer, POS, Partial Parser
CROSSLINGUAL RETRIEVAL
CONVERTOR 2
Lexikon MT BG DT
Lexicon EN
Documents SCORM
Ontology
Documents HTML
Pseudo-Struct
Glossary
Lexikon CZ
CONVERTOR 1
Metadata (Keywords)
Ling. Annot XML EN
Lexikon PT CZ
Lexikon RO MT PL
Lexikon PL DT GE
Lexicon GE RO BG EN
Documents User
(PDF, DOC, HTML,
SCORM,XML) PT
REPOSITORY

10. Creation of a learning objects archive
collection of the learning material (uploads & updates at - passwd protected)
IST domains for the LOs:
1. Use of computers in education, with sub-domains:
1.1 Teaching academic skills, with sub-domains:
1.1.1 Academic skills
1.1.2 Relevant computer skills for the above tasks (MS Word, Excel, Power Point, LaTex, Web pages, XML)
1.1.3 Basic computer skills (use of computer for beginners) (chats, , Intenet)
1.2 e-Learning, e-Marketing
1.3 The I*Teach document (Leonardo project,
1.4 Impact of use of computers in society
1.5 Studies about use of computers in schools / high schools
1.6 Impact of e-Learning on education
2. Calimera documents (parallel corpus developed in the Calimera FP5 project, )

11. Collection of learning materials and linguistic tools
normalization of the learning material
convertors from html/txt to basic XML format
Inventarization and classification of existing tools ( relevant to:
the integration of language technology resources in eLearning
the integration of semantic knowledge
Inventarization and classification of existing language resources corpora and frequencies lists:
lexica:

12. LMS User Profile LING. PROCESSOR CROSSLINGUAL RETRIEVAL CONVERTOR 2
Documents SCORM
Pseudo-Struct.
Basic XML
LING. PROCESSOR
Lemmatizer, POS, Partial Parser
CROSSLINGUAL RETRIEVAL
CONVERTOR 2
Lexikon MT BG DT
Lexicon EN
Documents SCORM
Ontology
Documents HTML
Pseudo-Struct
Glossary
Lexikon CZ
CONVERTOR 1
Metadata (Keywords)
Ling. Annot XML EN
Lexikon PT CZ
Lexikon RO MT PL
Lexikon PL DT GE
Lexicon GE RO BG EN
Documents User
(PDF, DOC, HTML,
SCORM,XML) PT
REPOSITORY

13. Semi-automatic metadata generation with LT and NLP
Aims:
supporting authors in the generation of metadata for LOs
improving keyword-driven search for LOs
supporting the development of glossaries for learning material

14. Metadata
metadata are essential to make LOs visible for larger groups of users
authors are reluctant or not experienced enough to supply them
NLP tools will help them in that task
the project uses the LOM metadata schema as a blueprint

15. Subtask 1: Identification of keywords
Good keywords have a typical, non random distribution in and across LOs
Keywords tend to appear more often at certain places in texts (headings etc.)
Keywords are often highlighted / emphasised by authors

16. Modelling Keywordiness
Residual Inverse document frequency used to model inter text distribution of KW
Term burstiness used to model intra text distribution of KW
Knowledge of text structure used to identify salient regions (e,g, headings)
Layout features of texts used to identify emphasised words and weight them higher

17. Challenges
Treating multi word keywords (suffix arrays will be used to identify n-grams of arbitrary length)
Assigning a combined weight which takes into account all the aforementioned factors
Multilinguality
Evaluation
manually assigned keywords will be used to measure precision and recall of key word extractor
Human annotator to judge results from extractor and rate them

18. Subtask 2: Identification of definitory contexts
Empirical approach based on the linguistic annotation of LOs
Identification of definitory contexts is language specific
Workflow
Definitory contexts is searched and marked in LOs (manually)
Local grammars are drafted on the basis of these examples
The linguistic annotation is used for these grammars
The grammars are applied to new Los
Extraction of definitory context performed by Lxtransduce (University of Edinburgh - LTG)

19. LMS User Profile LING. PROCESSOR CROSSLINGUAL RETRIEVAL CONVERTOR 2
Documents SCORM
Pseudo-Struct.
Basic XML
LING. PROCESSOR
Lemmatizer, POS, Partial Parser
CROSSLINGUAL RETRIEVAL
CONVERTOR 2
Lexikon MT BG DT
Lexicon EN
Documents SCORM
Ontology
Documents HTML
Pseudo-Struct
Glossary
Lexikon CZ
CONVERTOR 1
Metadata (Keywords)
Ling. Annot XML EN
Lexikon PT CZ
Lexikon RO MT PL
Lexikon PL DT GE
Lexicon GE RO BG EN
Documents User
(PDF, DOC, HTML,
SCORM,XML) PT
REPOSITORY

20. Ontology-based cross-lingual retrieval
Metadata can also be represented by ontologies
Creation of a domain ontology in the domain of LOs
For consistency reasons we employ also an upper ontology (DOLCE)
Lexical material in all 9 languages is mapped on the ontology and on the upper ontology
Ontology will allow for multilingual retrieval of LOs

21. Domain Ontology creation
 lexicon (vocabulary with natural language definitions);
simple taxonomy;
 thesaurus (taxonomy plus related-terms);
relational model (unconstrained use of arbitrary relations).
fully axiomatized theory.

22. Domain Ontology terminological dictionary in chosen domain
- term in English,
- a short definition in English
- translation of the term
formalize the definitions to reflect the relations like is-a, part-of, used-for;
definitions translated in OWL-DL
not achieve a fully axiomatized theory, but relational model of the domain
connection to the upper ontology will enforce the inheritance of the axiomatization of the upper ontology to the concepts in the domain ontology.

23. Upper Ontology: DOLCE
the ontology should be constructed on rigorous basis;
it should be easy to be represented as an ontological language such as RDF or OWL;
there are domain ontologies constructed with respect to it;
it can be related to lexicons - either by definition, or by already existing mapping to some lexical resource

24. Mapping multilingual resources on the domain ontology -1-
Trivial for words having exact a correspondent in the ontology
Problems appear when:
One word in a language sub-sums two or more concepts in the ontology
One word in a language sub-sums two or more concepts in an ontology but only in relations with some other concepts
One word has a much restrictive meaning not present in the ontology

25. Mapping multilingual resources on the domain ontology -2-
Solution to 1:
Express the lexical items in OWL-DL expressions: disjunction, conjunctions of classes (give example)
Solution to 2:
Express the lexical items in OWL-DL using together with operations on classes also relations between the involved concepts
Solution to 3:
Insert new concept in the ontology

26. Ontology enrichment
If one word cannot be mapped directly on the ontology look if a similar meaning can be retrieved in some other languages.
If this seems to be not an isolated case insert the new concept in the ontology.
In any case assign to each concept a label indicated the languages in which this concept is lexicalised

27. LMS User Profile LING. PROCESSOR CROSSLINGUAL RETRIEVAL CONVERTOR 2
Documents SCORM
Pseudo-Struct.
Basic XML
LING. PROCESSOR
Lemmatizer, POS, Partial Parser
CROSSLINGUAL RETRIEVAL
CONVERTOR 2
Lexikon MT BG DT
Lexicon EN
Documents SCORM
Ontology
Documents HTML
Pseudo-Struct
Glossary
Move CROSSLing retrieval on the same level with Ling resources
Retrieved Doc are original Docuemnts
Lexikon CZ
CONVERTOR 1
Metadata (Keywords)
Ling. Annot XML EN
Lexikon PT CZ
Lexikon RO MT PL
Lexikon PL DT GE
Lexicon GE RO BG EN
Documents User
(PDF, DOC, HTML,
SCORM,XML) PT
REPOSITORY

28. Integration in ILIAS
Integration of LT4eL functionalities for semi-automated metadata generation, definitory context extraction and ontology supported extended data retrieval into a learning management system (prototype based on ILIAS LMS)
Developing and providing documentation for a standard-technology-based interface between the language technology tools and learning management systems

29. Integration of functionalities
Development Server (CVS)
Content Portal
KW/DC
Ontology
ILIAS
LOs
Code
Code/Data
Code
Migration Tool
Nightly Updates
Use
functionalities through SOAP
Java Webserver (Tomcat)
ILIAS Server
Application
Logic
Webservices
nuSoap
LOs
Axis
KW/DC/Onto
Java Classes / Data
Evaluate functionalities in ILIAS
Third Party Tools
User Interface
Servlets/JSP
Evaluate functionalities directly

30. Validation of enhanced LMS.
Challenge is to answer these questions:
How does this compare with what can already be done with existing systems?
What added value is there?
What is the educational / pedagogic value of these functionalities?
Problem is to evaluate the functionality and separate from issues of usability or unfamiliarity with the LMS platform.How can we expect users to identify any benefit?

31. How can we expect users to identify any benefit?
Present them with tasks to complete using LMS
With no project functionality
With project functionality
Partial
Full
Identify potential users
Course Creators
Content Authors or Providers
Teachers
Students
studying in their own language
studying in a second language

32. Create outline User Scenarios
We define scenarios, in this context, as
a story focused on a user or group of users which provides information on
the nature of the users,
the goals they wish to achieve and
the context in which the activities will take place.
They are written in ordinary language, and are therefore understandable to various stakeholders, including users.
They may also contain different degrees of detail.

33. Project Plan Preparatory work in place (May 06).
Development functionalities complete (November 2006).
Integration functionalities in LMS complete (May 2007)
First cycle integration functionalities in LMS and their validation complete (November 2007)
Second cycle integration functionalities in LMS and their validationcomplete (May 2008)

34. Contact
Contact for information: