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

Shashi Narayan, Claire Gardent

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

Abstract / Description of output

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 treehighlights 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.
Original languageEnglish
Title of host publicationProceedings of the 24th International Conference on Computational Linguistics (COLING)
Place of PublicationMumbai, India
PublisherThe COLING 2012 Organizing Committee
Pages2011-2026
Number of pages16
Publication statusPublished - 1 Dec 2012
Event24th International Conference on Computational Linguistics - IIT Bombay, Mumbai, India
Duration: 8 Dec 201215 Dec 2012
http://www.coling2012-iitb.org/

Conference

Conference24th International Conference on Computational Linguistics
Abbreviated titleCOLING 2012
Country/TerritoryIndia
CityMumbai
Period8/12/1215/12/12
Internet address

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