@incollection{Akhmatova:2006,
	title = {Recognizing Textual Entailment via Atomic Propositions},
	address = {Berlin},
	author = {Elena Akhmatova and Diego Moll{\'a}},
	booktitle = {Machine Learning Challenges},
	pages = {385-403},
	publisher = {Springer},
	series = {Lecture Notes in Computer Science},
	url = {http://www.springerlink.com/content/wl4071pj235h7w02/},
	volume = {3944/2006},
	year = {2006},
	biburl = {http://www.bibsonomy.org/bibtex/2edc87f2607b4cfab04ee44b4c72f5a9a/diego_ma},
	abstract = {This paper describes Macquarie University’s Centre for Language Technology contribution to the PASCAL 2005 Recognizing Textual Entailment challenge. Our main aim was to test the practicability of a purely logical approach. For this, atomic propositions were extracted from both the text and the entailment hypothesis and they were expressed in a custom logical notation. The text entails the hypothesis if every proposition of the hypothesis is entailed by some proposition in the text. To extract the propositions and encode them into a logical notation the system uses the output of Link Parser. To detect the independent entailment relations the system relies on the use of Otter and WordNet.},
	keywords = {entailment molla_publication }
}