WordsEye: an automatic text-to-scene conversion system
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Practical experiments with regular approximation of context-free languages
Computational Linguistics - Special issue on finite-state methods in NLP
Three generative, lexicalised models for statistical parsing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
PRINCIPAR: an efficient, broad-coverage, principle-based parser
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Robust understanding in multimodal interfaces
Computational Linguistics
On the learnability of shuffle ideals
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
On the learnability of shuffle ideals
The Journal of Machine Learning Research
| Hi-index | 0.00 |
Parsli is a finite-state (FS) parser which can be tailored to the lexicon, syntax, and semantics of a particular application using a hand-editable declarative lexicon. The lexicon is defined in terms of a lexicalized Tree Adjoining Grammar, which is subsequently mapped to a FS representation. This approach gives the application designer better and easier control over the natural language understanding component than using an off-the-shelf parser. We present results using Parsli on an application that creates 3D-images from typed input.