lt4el - integrating language technology and semantic web techniques in elearning
DESCRIPTION
LT4EL - Integrating Language Technology and Semantic Web techniques in eLearning. Lothar Lemnitzer GLDV AK eLearning, 11. September 2007. LT4eL - Language Technology for eLearning. Start date: 1 December 2005 Duration: 30 months Partners: 12 EU finacing: 1.5 milion Euro - PowerPoint PPT PresentationTRANSCRIPT
LT4EL - Integrating Language Technology and Semantic Web
techniques in eLearningLothar Lemnitzer
GLDV AK eLearning, 11. September 2007
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 027391
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
LT4eL- Objectives -1-
• Scientific and Technological Objectives– Integration of language technology
resources and tools in eLearning– Integration of semantic Knowledge in
eLearning– Improve (multilingual) retrieval of
learning material
LT4eL - Languages
• Bulgarian• Czech• Dutch• German• Maltese• Polish• Portuguese• Romanian• English
LT4eL- Objectives -2-
• Political objectives– Support multilinguality– Knowledge transfer– Awareness raising– Exploitation of resources– Facilitate access to education
Tasks
• Creation of an archive of learning objects• Semi-automatic metadata generation driven
by NLP tools:– Keyword extractor– Definition extractor
• Enhancing eLearning with semantic knowledge– ontologies
• Integration of functionalities in the ILIAS Learning Management System;
• Validation of new functionalities in the ILIAS Learning Management System;
• Address Multilinguality
Lexikon
CZ
CZCZEN
ENCONVERTOR 1
Documents SCORM
Pseudo-Struct.
Basic XML LING. PROCESSOR
Lemmatizer, POS, Partial Parser
CROSSLINGUAL RETRIEVAL
LMS User Profile
Documents SCORM
Pseudo-Struct
Metadata (Keywords)
Ling. Annot XML
Ontology
CONVERTOR 2
Documents HTML
Lexikon
PT
Lexikon
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Lexikon
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Lexicon
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Lexikon
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Lexikon
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Lexicon
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ENDocuments User
(PDF, DOC, HTML,
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REPOSITORY
Glossary
Creation of a learning objects archive • collection of the learning material (uploads & updates at
http://consilr.info.uaic.ro/uploads_lt4el/ - 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, e-mail, Intenet)• 1.2 Impact of e-Learning on education
2. Calimera documents (parallel corpus developed in the Calimera FP5 project, http://www.calimera.org/ )
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 (
http://consilr.info.uaic.ro/uploads_lt4el/tools/all.php?) 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: http://consilr.info.uaic.ro/uploads_lt4el/menu/all.php
• lexica: http://www.let.uu.nl/lt4el/wiki/index.php/Lexica_Joint_Table
Lexikon
CZ
CZCZEN
ENCONVERTOR 1
Documents SCORM
Pseudo-Struct.
Basic XML LING. PROCESSOR
Lemmatizer, POS, Partial Parser
CROSSLINGUAL RETRIEVAL
LMS User Profile
Documents SCORM
Pseudo-Struct
Metadata (Keywords)
Ling. Annot XML
Ontology
CONVERTOR 2
Documents HTML
Lexikon
PT
Lexikon
RO
Lexikon
PL
Lexicon
GE
Lexikon
MT
Lexikon
BG
Lexikon
DT
Lexicon
EN
PLPL
GEGE
BGBG
PTPT
MTMT
DTDT
RORO
ENDocuments User
(PDF, DOC, HTML,
SCORM,XML)
REPOSITORY
Glossary
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
Metadata
• metadata is essential to make LOs visible for larger groups of users
• authors are reluctant or not experienced enough to supply it
• NLP tools will help them in that task• the project uses the LOM metadata
schema as a blueprint
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
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
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
Identification of definitory contexts
• Empirical approach based on linguistic annotation of LO• Identification of definitory contexts is language specific• Workflow
– Definitory contexts are searched and marked in LOs (manually)
– Local grammars are drafted on the basis of these examples
– Linguistic annotation is used for these grammars– Grammars are applied to new LOs– Extraction of definitory context performed by
Lxtransduce (University of Edinburgh - LTG)
Lexikon
CZ
CZCZEN
ENCONVERTOR 1
Documents SCORM
Pseudo-Struct.
Basic XML LING. PROCESSOR
Lemmatizer, POS, Partial Parser
CROSSLINGUAL RETRIEVAL
LMS User Profile
Documents SCORM
Pseudo-Struct
Metadata (Keywords)
Ling. Annot XML
Ontology
CONVERTOR 2
Documents HTML
Lexikon
PT
Lexikon
RO
Lexikon
PL
Lexicon
GE
Lexikon
MT
Lexikon
BG
Lexikon
DT
Lexicon
EN
PLPL
GEGE
BGBG
PTPT
MTMT
DTDT
RORO
ENDocuments User
(PDF, DOC, HTML,
SCORM,XML)
REPOSITORY
Glossary
Ontology-based cross-lingual retrieval
• Metadata can also be represented by ontologies• Creation of a domain ontology in the area 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
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
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
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
Lexikon
CZ
CZCZEN
ENCONVERTOR 1
Documents SCORM
Pseudo-Struct.
Basic XML LING. PROCESSOR
Lemmatizer, POS, Partial Parser
CROSSLINGUAL RETRIEVAL
LMS User Profile
Documents SCORM
Pseudo-Struct
Metadata (Keywords)
Ling. Annot XML
Ontology
CONVERTOR 2
Documents HTML
Lexikon
PT
Lexikon
RO
Lexikon
PL
Lexicon
GE
Lexikon
MT
Lexikon
BG
Lexikon
DT
Lexicon
EN
PLPL
GEGE
BGBG
PTPT
MTMT
DTDT
RORO
ENDocuments User
(PDF, DOC, HTML,
SCORM,XML)
REPOSITORY
Glossary
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
Integration of functionalities
ILIAS ServerJava Webserver (Tomcat)
ApplicationLogic
User Interface
KW/DC/OntoJava
Classes/ Data
Webservices
AxisnuSoap
Servlets/JSP
Development Server (CVS)
KW/DC
Code Code/Data
Ontology
Code
ILIAS
Content Portal
LOs
LOs
Evaluatefunctionalities directly
Evaluate functionalitiesin ILIAS
Nightly Updates
Usefunctionalities
throughSOAP
Migration Tool
ThirdPartyTools
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?
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
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.
Conclusions• 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