@inproceedings{01190cd66eb54dee9efbdae75cf525d1,
title = "Normalized Alignment of Dependency Trees for Detecting Textual Entailment",
abstract = "In this paper, we investigate the usefulness of normalized alignment of dependency trees for entailment prediction. Overall, our approach yields an accuracy of 60\% on the RTE2 test set, which is a significant improvement over the baseline. Results vary substantially across the different subsets, with a peak performance on the summarization data. We conclude that normalized alignment is useful for detecting textual entailments, but a robust approach will probably need to include additional sources of information.",
keywords = "METIS-238159, EWI-6882, IR-66341",
author = "E. Marsi and E. Krahmer and W.E. Bosma and Mariet Theune",
year = "2006",
month = apr,
language = "Undefined",
isbn = "not assigned",
publisher = "Springer",
number = "2",
pages = "56--61",
editor = "B. Magnini and I. Dagan",
booktitle = "Second PASCAL Recognising Textual Entailment Challenge",
address = "Germany",
note = "Second PASCAL Recognising Textual Entailment Challenge, Venice, Italy ; Conference date: 01-04-2006",
}