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Error Mining on Dependency Trees

Claire Gardent 1 Shashi Narayan 1 
1 SYNALP - Natural Language Processing : representations, inference and semantics
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : 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.
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Submitted on : Friday, December 21, 2012 - 8:51:47 AM
Last modification on : Saturday, October 16, 2021 - 11:26:06 AM
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  • HAL Id : hal-00768204, version 1



Claire Gardent, Shashi Narayan. Error Mining on Dependency Trees. 50th Annual Meeting of the Association for Computational Linguistics, Jul 2012, Jeju Island, South Korea. pp.592-600. ⟨hal-00768204⟩



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