Error Mining on Dependency Trees

Claire Gardent, Shashi Narayan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationProceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Place of PublicationJeju Island, Korea
PublisherAssociation for Computational Linguistics
Number of pages9
Publication statusPublished - 1 Jul 2012
Event50th Annual Meeting of the Association for Computational Linguistics - Jeju Island, Korea, Republic of
Duration: 8 Jul 201214 Jul 2012


Conference50th Annual Meeting of the Association for Computational Linguistics
Country/TerritoryKorea, Republic of
CityJeju Island


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