Understanding Natural Language
Understanding Natural Language
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Semiotic schemas: a framework for grounding language in action and perception
Artificial Intelligence - Special volume on connecting language to the world
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
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Semantic discourse segmentation and labeling for route instructions
COLING ACL '06 Proceedings of the 21st International Conference on computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Spatial language for human-robot dialogs
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Following directions using statistical machine translation
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
Towards automatic functional test execution
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Fast online lexicon learning for grounded language acquisition
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Unsupervised PCFG induction for grounded language learning with highly ambiguous supervision
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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We have developed a simulation model that accepts instructions in unconstrained natural language, and then guides a robot to the correct destination. The instructions are segmented on the basis of the actions to be taken, and each segment is labeled with the required action. This flat formulation reduces the problem to a sequential labeling task, to which machine learning methods are applied. We propose an innovative machine learning method for explicitly modeling the actions described in instructions and integrating learning and inference about the physical environment. We obtained a corpus of 840 route instructions that experimenters verified as follow-able, given by people in building navigation situations. Using the four-fold cross validation, our experiments showed that the simulated robot reached the correct destination 88% of the time.