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.
Type de document :
Communication dans un congrès
50th Annual Meeting of the Association for Computational Linguistics, Jul 2012, Jeju Island, South Korea. pp.592-600, 2012
Liste complète des métadonnées

Littérature citée [11 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-00768204
Contributeur : Claire Gardent <>
Soumis le : vendredi 21 décembre 2012 - 08:51:47
Dernière modification le : mardi 24 avril 2018 - 13:33:22
Document(s) archivé(s) le : vendredi 22 mars 2013 - 03:45:23

Fichier

acl12-errormining.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00768204, version 1

Collections

Citation

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, 2012. 〈hal-00768204〉

Partager

Métriques

Consultations de la notice

248

Téléchargements de fichiers

109