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Towards a Better Understanding of Noise in Natural Language Processing
In this paper, we propose a definition and taxonomy of various types of non-standard textual content – generally referred to as “noise” – in Natural Language Processing (NLP).
A Taxonomy and Study of Critical Errors in Machine Translation
We also study the impact of the source text on generating critical errors in the translation and, based on this, propose a set of recommendations on aspects of the MT that need further scrutiny, especially for user-generated content, to avoid generating such errors, and hence improve online communication.
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