Amanote Research
Register
Sign In
Discover open access scientific publications
Search, annotate, share and cite publications
Publications by Kathrin Spreyer
Data-Driven Dependency Parsing of New Languages Using Incomplete and Noisy Training Data
Related publications
On the Complexity of Non-Projective Data-Driven Dependency Parsing
A New Reliability-Based Data-Driven Approach for Noisy Experimental Data With Physical Constraints
Computer Methods in Applied Mechanics and Engineering
Mechanics of Materials
Mechanical Engineering
Computer Science Applications
Computational Mechanics
Astronomy
Physics
A Robust Classification of Galaxy Spectra: Dealing With Noisy and Incomplete Data
Astronomical Journal
Astrophysics
Astronomy
Planetary Science
Space
Dynamic Data-Driven Model Reduction: Adapting Reduced Models From Incomplete Data
Advanced Modeling and Simulation in Engineering Sciences
Modeling
Applied Mathematics
Computer Science Applications
Engineering
Simulation
Improved Hopfield Networks by Training With Noisy Data
Performance of Inductive Method of Model Self-Organization With Incomplete Model and Noisy Data
Multilingual Dependency Parsing Using Bayes Point Machines
A New System for Massive RDF Data Management Using Big Data Query Languages Pig, Hive, and Spark
International Journal of Computing and Digital Systems
Computer Graphics
Human-Computer Interaction
Computer Networks
Communications
Information Systems
Computer-Aided Design
Innovation
Management of Technology
Artificial Intelligence
Identifying Cascading Errors Using Constraints in Dependency Parsing