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2008

Cheap and Fast - But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks

11 years 3 months ago
Cheap and Fast - But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks
Human linguistic annotation is crucial for many natural language processing tasks but can be expensive and time-consuming. We explore the use of Amazon's Mechanical Turk system, a significantly cheaper and faster method for collecting annotations from a broad base of paid non-expert contributors over the Web. We investigate five tasks: affect recognition, word similarity, recognizing textual entailment, event temporal ordering, and word sense disambiguation. For all five, we show high agreement between Mechanical Turk non-expert annotations and existing gold standard labels provided by expert labelers. For the task of affect recognition, we also show that using non-expert labels for training machine learning algorithms can be as effective as using gold standard annotations from experts. We propose a technique for bias correction that significantly improves annotation quality on two tasks. We conclude that many large labeling tasks can be effectively designed and carried out in th...
Rion Snow, Brendan O'Connor, Daniel Jurafsky, Andr
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where EMNLP
Authors Rion Snow, Brendan O'Connor, Daniel Jurafsky, Andrew Y. Ng
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