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 Type project: STREP IST

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 EN CONVERTOR 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 PLGE BG PTMTDTRO EN Documents User (PDF, DOC, HTML, SCORM,XML) REPOSITORY Glossary

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: Academic skills Relevant computer skills for the above tasks (MS Word, Excel, Power Point, LaTex, Web pages, XML) Basic computer skills (use of computer for beginners) (chats, , Intenet) 1.2 Impact of e-Learning on education 2. Calimera documents (parallel corpus developed in the Calimera FP5 project, )

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:

Lexikon CZ EN CONVERTOR 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 PLGE BG PTMTDTRO EN Documents 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 EN CONVERTOR 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 PLGE BG PTMTDTRO EN Documents 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 EN CONVERTOR 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 PLGE BG PTMTDTRO EN Documents 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 Server Java Webserver (Tomcat) Application Logic User Interface KW/DC/Onto Java Classes / Data Webservices Axis nuSoap Servlets/JSP Development Server (CVS) KW/DC Code Code/Data Ontology Code ILIAS Content Portal LOs Evaluate functionalities directly Evaluate functionalities in ILIAS Nightly Updates Use functionalities through SOAP Migration Tool Third Party Tools

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

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