Just to remind you, because Monday is tomorrow:

Title: Cheap and Fast But is it Good? Evaluating Nonexpert Annotations for Machine Learning Tasks Speaker: Rion Snow Date: Monday, November 17th, 15:00 Place: Room 3195 (pav. André-Aisenstadt, UdeM)

Abstract: Human annotation is crucial for many machine learning tasks but can be expensive and time-consuming. We explore the use of Amazon's Mechanical Turk web service, a significantly cheaper and faster method for collecting annotations from a broad base of paid non-expert contributors over the Web. We investigate five task in the field of natural language processing: 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 this method at a fraction of the usual expense.

A summary of this work may be found online at: http://blog.doloreslabs.com/2008/09/amt-fast-cheap-good-machine-learning/.

Bio: Rion Snow is a PhD Candidate in Computer Science at Stanford University, advised by Professors Andrew Ng and Dan Jurafsky. Rion works in the intersection of machine learning and natural language processing, with a focus in computational semantics. He leads the Stanford Wordnet Project, which aims at learning large-scale semantic networks automatically from natural text. His work on automatically inferring semantic taxonomies received the Best Paper Award at the 2006 conference for the Association of Computational Linguistics. His publications and contact information may be found at his home page: http://ai.stanford.edu/~rion/.