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EMNLP
2004
13 years 11 months ago
Learning Hebrew Roots: Machine Learning with Linguistic Constraints
The morphology of Semitic languages is unique in the sense that the major word-formation mechanism is an inherently non-concatenative process of interdigitation, whereby two morph...
Ezra Daya, Dan Roth, Shuly Wintner
ML
2010
ACM
135views Machine Learning» more  ML 2010»
13 years 4 months ago
Multi-domain learning by confidence-weighted parameter combination
State-of-the-art statistical NLP systems for a variety of tasks learn from labeled training data that is often domain specific. However, there may be multiple domains or sources o...
Mark Dredze, Alex Kulesza, Koby Crammer
IAT
2007
IEEE
14 years 4 months ago
Learn to Detect Phishing Scams Using Learning and Ensemble ?Methods
Phishing attack is a kind of identity theft which tries to steal confidential data like on-line bank account information. In a phishing attack scenario, attacker deceives users by...
Alireza Saberi, Mojtaba Vahidi, Behrouz Minaei-Bid...
ECTEL
2007
Springer
14 years 3 months ago
Representing Computer-Supported Collaborative Learning Macro-scripts using IMS Learning Design
IMS Learning Design (LD) is a specification that aims at computationally representing any learning process. However, the possibilities of LD to represent collaborative learning sce...
Davinia Hernández Leo, Daniel Burgos, Colin...
ITS
2004
Springer
129views Multimedia» more  ITS 2004»
14 years 3 months ago
Developing Learning by Teaching Environments That Support Self-Regulated Learning
Abstract. Betty’s Brain is a teachable agent system in the domain of river ecosystems that combines learning by teaching and self-regulation strategies to promote deep learning a...
Gautam Biswas, Krittaya Leelawong, Kadira Belynne,...