Abstract
We present a novel Evaluation Metric for Morphological Analysis (EMMA) that is
both linguistically appealing and empirically sound. EMMA uses a graph-based
assignment algorithm, optimized via integer linear programming, to match
morphemes of predicted word analyses to the analyses of a morphologically rich
answer key. This is necessary especially for unsupervised morphology analysis
systems which do not have access to linguistically motivated morpheme labels.
Across 3 languages, EMMA scores of 14 systems have a substantially greater
positive correlation with mean average precision in an information retrieval
(IR) task than do scores from the metric currently used by the Morpho Challenge
(MC) competition series. We compute EMMA and MC metric scores for 93 separate
system-language pairs from the 2007, 2008, and 2009 MC competitions,
demonstrating that EMMA is not susceptible to two types of gaming that have
plagued recent MC competitions: Ambiguity Hijacking and Shared Morpheme
Padding. The EMMA evaluation script is publicly available from
http://www.cs.bris.ac.uk/Research/MachineLearning/Morphology/Resources/.
Translated title of the contribution | EMMA: A Novel Evaluation Metric for Morphological Analysis |
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Original language | English |
Title of host publication | Proceedings of the 23rd International Conference on Computational Linguistics (COLING) |
Publication status | Published - 2010 |
Bibliographical note
Other page information: -Conference Proceedings/Title of Journal: Proceedings of the 23rd International Conference on Computational Linguistics (COLING)
Other identifier: 2001226