WIMS'11 - dag 3

Ashwin Ram: Open Social Learning Communities
Open Social Learning, what's the problem?
- access (we're not able to meet the demand for education)
- dropouts (the education is not interesting enough)

The Long Tail of Education
- "Short Head" dominated by major players
- Long Tail consists of among others a range of online courses (MIT, iTunes U, Open Yale

Problem with online courses is the one-way delivering of information an no interaction
We have to learn from online games and other popular web activities for young people, and build educational systems like that.

Open Study is an initiative that tries to do this.
- Open Study is a social platform for learners who want to help each other study
- two important aspects: social and gamelike
- launched in Sept 2010 and has already > 50 000 users


Technologies behind:
- Really Real-time Collaboration
- AI Recommendation Engine
- Social Media Analysis
- Social Capital Engine

Focusing on the features that really helps the user and throw away the rest (typically less features for every new update)

Platform
MongoDB
Scala Fraqmewordk
Lift web framework (Scala)
- all this is hosted on Amazon's Cloud System

Economy
- online education is a very large market

- the business models are rooted in the old world of education

- most of today's online courses are funded by Foundations
- have to look to certificates (be able to grant certificates for courses)
- students are also asking for services that could be provided for payment
- universities as we know today will have a quite different role - they will not disappear but must adapt to an online education reality

Marko Grobelnik: Many Faces of Text Processing

Different approaches to text processing:
- Computational Linguistics (language)
- web 2.0 (community)
- Sem Web (interoperbility)
- Text Minining (analytics)
- Machine Learning (statistiscs)
- Information Retrieval (search)
- Social Networks Analysis (graphs/networks)

Methodological approaches:
Top-down (Sem Web, KRR)
Bottom-up (Machine Learning, Data Mining)
Collaborative approach (web 2.0, Social Computing)

Levels of text representation:
- Lexical (character, words, phrases, part-of-speech tags, taxonomies/thesauri)
- Syntactic (vector-space model, language models, full-parsing, cross-modality
- Semantic (collaborative tagging/web 2.0, templates/frames, ontologies/first order theories)

The majority of the market actors today works on the syntactic (and lexical) level.

Marko then goes on to demonstrate the different approaches to text processing by showing a range of demos.

- Enrych: Enriching
- Searchpoint: Crawls the next result pages (a few hundred results) and clusters them in different categories on the fly, or classifying them with dmoz.org categories (you should try it! searchpoint.ijs.si)
- News reporting bias
- News visualization (News Explorer - will be available on the web soon)
- Knowledge based summarization
- Question and Answering: Demo showing how triples are extracted from documents given the NLP search and the result shows the triples, the corresponding text and the text repr. of the triples
- The Cyc Ontology ("The Ontology of the World")
   - 15 000 predicates (this is the hard part!)
   - 300 000 concepts
   - 3,2 mill assertions

More information on VideoLectures.net