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...

• Type: Conference
• Year: 2008
• Where: EMNLP
• Authors: Rion Snow, Brendan O'Connor, Daniel Jurafsky, Andrew Y. Ng
• Keywords: Annotation, EMNLP 2008, Mechanical Turk, Natural Language Processing, Turk Non-expert Annotations