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Error Mining with Suspicion Trees: Seeing the Forest for the Trees

Shashi Narayan 1 Claire Gardent 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 have been proposed to identify the most likely sources of errors in symbolic parsers and generators. However the techniques used generate a flat list of suspicious forms ranked by decreasing order of suspicion. We introduce a novel algorithm that structures the output of error mining into a tree (called, suspicion tree) highlighting the relationships between suspicious forms. We illustrate the impact of our approach by applying it to detect and analyse the most likely sources of failure in surface realisation; and we show how the suspicion tree built by our algorithm helps presenting the errors identified by error mining in a linguistically meaningful way thus providing better support for error analysis. The right frontier of the tree highlights the relative importance of the main error cases while the subtrees of a node indicate how a given error case divides into smaller more specific cases
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Submitted on : Friday, December 21, 2012 - 9:22:39 AM
Last modification on : Tuesday, December 18, 2018 - 4:38:01 PM
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Shashi Narayan, Claire Gardent. Error Mining with Suspicion Trees: Seeing the Forest for the Trees. 24th International Conference on Computational Linguistics, Dec 2012, Mumbai, India. pp.60-73. ⟨hal-00768227⟩

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