Building natural language generation systems
Building natural language generation systems
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
The interface between phrasal and functional constraints
Computational Linguistics
HMM-based word alignment in statistical translation
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Combining clues for word alignment
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Bootstrapping lexical choice via multiple-sequence alignment
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Statistical acquisition of content selection rules for natural language generation
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Collective content selection for concept-to-text generation
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Journal of Location Based Services
A NLG-based application for walking direction
ACLDemos '09 Proceedings of the ACL-IJCNLP 2009 Software Demonstrations
Learning semantic correspondences with less supervision
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Creating an annotated corpus for generating walking directions
UCNLG+Sum '09 Proceedings of the 2009 Workshop on Language Generation and Summarisation
Learning to follow navigational directions
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Landmarks in OpenLS — a data structure for cognitive ergonomic route directions
GIScience'06 Proceedings of the 4th international conference on Geographic Information Science
Modeling spatial knowledge for generating verbal and visual route directions
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IV
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Route directions are natural language (NL) statements that specify, for a given navigational task and an automatically computed route representation, a sequence of actions to be followed by the user to reach his or her goal. A corpus-based approach to generate route directions involves (i) the selection of elements along the route that need to be mentioned, and (ii) the induction of a mapping from route elements to linguistic structures that can be used as a basis for NL generation. This paper presents an Expectation-Maximization (EM) based algorithm that aligns geographical route representations with semantically annotated NL directions, as a basis for the above tasks. We formulate one basic and two extended models, the latter capturing special properties of the route direction task. Although our current data set is small, both extended models achieve better results than the simple model and a random baseline. The best results are achieved by a combination of both extensions, which outperform the random baseline and the simple model by more than an order of magnitude.