1 / 5

Robust Algorithms for Natural Language Processing Research at Växjö University

Explore research at Växjö University's School of Mathematics and Systems Engineering, focusing on developing robust and efficient algorithms for natural language processing. Discover the importance of machine learning in improving accuracy and the need for Swedish treebanks for training and validation.

pclowers
Download Presentation

Robust Algorithms for Natural Language Processing Research at Växjö University

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Växjö University Växjö University Joakim Nivre

  2. Who? • Växjö University (800) • School of Mathematics and Systems Engineering (120) • Computer Science division (30) • Language Technology group (5): • Models and Algorithms in Language Technology (MALT)

  3. Why? • Main focus of research: • Robust and efficient algorithms for natural language processing • Machine learning to improve accuracy • Need for treebanks: • Training and validation in machine learning • Evaluation of accuracy • No large treebank available for Swedish!

  4. What? • Projects • Swedish Treebank: • Pilot project funded by The Bank of Sweden Tercentennary Foundation (RJ) • Symposium in Växjö, November 2002 • Project proposal to RJ, March 2003 • Stochastic Dependency Grammars: • Theoretical properties of dependency grammars • Robust and efficient parsing algorithms • Machine learning to improve parsing accuracy

  5. What? • Corpora: • SynTag converted to dependency trees: • 100 k words, manually annotated (Järborg 1986) • Automatic conversion to dependency trees • Tools: • Trainable part-of-speech tagger • Efficient in training and tagging • Suffix model for unknown words • Dependency parser (under development) • Linear time projective dependency parsing • Trainable through external parse table

More Related