6;909 Reasons to Mess Up Your Data

Anders Søgaard
Københavns Universitet, Denmark

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Ingår i: Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013); May 22-24; 2013; Oslo University; Norway. NEALT Proceedings Series 16

Linköping Electronic Conference Proceedings 85:3, s. 5-5

NEALT Proceedings Series 16:3, s. 5-5

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Publicerad: 2013-05-17

ISBN: 978-91-7519-589-6

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


In computational linguistics we develop tools and on-line services for everything from literature to social media data; but our tools are often optimized to minimize expected error on a single annotated dataset; typically newspaper articles - and evaluated on held-out data sampled from the same dataset. Significance testing across data points randomly sampled from a standard dataset only tells us how likely we are to see better performance on more data points sampled this way; but says nothing about performance on other datasets. This talk discusses how to modify learning algorithms to minimize expected error on future; unseen datasets; with applications to PoS tagging and dependency parsing; including cross-language learning problems. It also discusses the related issue of how to best evaluate NLP tools (intrinsically) taking their possible out-of-domain applications into account.


Domain Variation; PoS Tagging; Dependency Parsing; Evaluation


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