@conference {12405,
	title = {Measuring transitivity using untrained annotators},
	booktitle = {Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon{\textquoteright}s Mechanical Turk},
	series = {CSLDAMT {\textquoteright}10},
	year = {2010},
	month = {2010///},
	pages = {188 - 194},
	publisher = {Association for Computational Linguistics},
	organization = {Association for Computational Linguistics},
	address = {Stroudsburg, PA, USA},
	abstract = {Hopper and Thompson (1980) defined a multi-axis theory of transitivity that goes beyond simple syntactic transitivity and captures how much "action" takes place in a sentence. Detecting these features requires a deep understanding of lexical semantics and real-world pragmatics. We propose two general approaches for creating a corpus of sentences labeled with respect to the Hopper-Thompson transitivity schema using Amazon Mechanical Turk. Both approaches assume no existing resources and incorporate all necessary annotation into a single system; this is done to allow for future generalization to other languages. The first task attempts to use language-neutral videos to elicit human-composed sentences with specified transitivity attributes. The second task uses an iterative process to first label the actors and objects in sentences and then annotate the sentences{\textquoteright} transitivity. We examine the success of these techniques and perform a preliminary classification of the transitivity of held-out data.},
	url = {http://dl.acm.org/citation.cfm?id=1866696.1866726},
	author = {Madnani,Nitin and Jordan Boyd-Graber and Resnik, Philip}
}
