2017
EACL
EACL 2017
A method for in-depth comparative evaluation: How (dis)similar are outputs of pos taggers, dependency parsers and coreference resolvers really?
Abstract
AbstractThis paper proposes a generic method for the comparative evaluation of system outputs. The approach is able to quantify the pairwise differences between two outputs and to unravel in detail what the differences consist of. We apply our approach to three tasks in Computational Linguistics, i.e. POS tagging, dependency parsing, and coreference resolution. We find that system outputs are more distinct than the (often) small differences in evaluation scores seem to suggest.
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The Questioner
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Interdisciplinary Bridge
— Interdisciplinary and Machine Learning and Natural Language Processing
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Trend Setter
— Evaluation
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Keyword Pioneer
— comparative evaluation
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Hot Topic Early Bird
— computational linguistics
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
Authors
Topics
Natural Language Processing > Understanding > Coreference Resolution
Natural Language Processing > Understanding > Parsing
Natural Language Processing > Resources & Methods > Text Representation
Interdisciplinary > Linguistics > Computational Linguistics
Machine Learning > Optimization & Theory > Evaluation