Foundations of statistical natural language processing
Foundations of statistical natural language processing
Understanding Natural Language
Understanding Natural Language
Training Personal Robots Using Natural Language Instruction
IEEE Intelligent Systems
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Introduction to the CoNLL-2000 shared task: chunking
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Learning to follow navigational route instructions
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Towards automatic functional test execution
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
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In order to build a simulated robot that accepts instructions in unconstrained natural language, a corpus of 427 route instructions was collected from human subjects in the office navigation domain. The instructions were segmented by the steps in the actual route and labeled with the action taken in each step. This flat formulation reduced the problem to an IE/Segmentation task, to which we applied Conditional Random Fields. We compared the performance of CRFs with a set of hand-written rules. The result showed that CRFs perform better with a 73.7% success rate.