SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Error mining for wide-coverage grammar engineering
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Error mining in parsing results
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Spotting overgeneration suspects
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
Evaluating coverage for large symbolic NLG grammars
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A generalized method for iterative error mining in parsing results
GEAF '09 Proceedings of the 2009 Workshop on Grammar Engineering Across Frameworks
RTG based surface realisation for TAG
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Comparing the performance of two TAG-based surface realisers using controlled grammar traversal
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
The first surface realisation shared task: overview and evaluation results
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
The OSU system for surface realization at Generation Challenges 2011
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
Generation for grammar engineering
INLG '12 Proceedings of the Seventh International Natural Language Generation Conference
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In recent years, error mining approaches were developed to help identify the most likely sources of parsing failures in parsing systems using handcrafted grammars and lexicons. However the techniques they use to enumerate and count n-grams builds on the sequential nature of a text corpus and do not easily extend to structured data. In this paper, we propose an algorithm for mining trees and apply it to detect the most likely sources of generation failure. We show that this tree mining algorithm permits identifying not only errors in the generation system (grammar, lexicon) but also mismatches between the structures contained in the input and the input structures expected by our generator as well as a few idiosyncrasies/error in the input data.