InforLorV4, Main, Exploration, bibRecord, 001A32

Error Mining on Dependency Trees

Identifieur interne : 001A32 ( Main/Exploration ); précédent : 001A31; suivant : 001A33

Error Mining on Dependency Trees

Auteurs : Claire Gardent [France] ; Shashi Narayan [France]

Source :

RBID : Hal:hal-00768204

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.

Url:

https://hal.archives-ouvertes.fr/hal-00768204

Affiliations:

Links toward previous steps (curation, corpus...)

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Le document en format XML

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<front><div type="abstract" xml:lang="en">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.</div>
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Error Mining on Dependency Trees

Error Mining on Dependency Trees

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Abstract

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