acl acl2011 acl2011-254 acl2011-254-reference knowledge-graph by maker-knowledge-mining
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Author: Or Biran ; Samuel Brody ; Noemie Elhadad
Abstract: We present a method for lexical simplification. Simplification rules are learned from a comparable corpus, and the rules are applied in a context-aware fashion to input sentences. Our method is unsupervised. Furthermore, it does not require any alignment or correspondence among the complex and simple corpora. We evaluate the simplification according to three criteria: preservation of grammaticality, preservation of meaning, and degree of simplification. Results show that our method outperforms an established simplification baseline for both meaning preservation and simplification, while maintaining a high level of grammaticality.
Androutsopoulos, Ion and Prodromos Malakasiotis. 2010. A survey of paraphrasing and textual entailment methods. Journal of Artificial Intelligence Research 38: 135–187. Barzilay, Regina and Noemie Elhadad. 2003. Sentence alignment for monolingual comparable cor- pora. In Proc. EMNLP. pages 25–32. Blake, Catherine, Julia Kampov, Andreas Orphanides, David West, and Cory Lown. 2007. Query expansion, lexical simplification, and sentence selection strategies for multi-document summarization. In Proc. DUC. Carroll, John, Guido Minnen, Yvonne Canning, Siobhan Devlin, and John Tait. 1998. Practical simplication of english newspaper text to assist aphasic readers. In Proc. AAAI Workshop on Integrating Artificial Intelligence and Assistive Technology. Chandrasekar, R., Christine Doran, and B. Srinivas. 1996. Motivations and methods for text simplification. In Proc. COLING. Daelemans, Walter, Anja Hthker, and Erik Tjong Kim Sang. 2004. Automatic sentence simplification for subtitling in Dutch and English. In Proc. LREC. pages 1045–1048. Del e´ger, Louise and Pierre Zweigenbaum. 2009. Extracting lay paraphrases of specialized expressions from monolingual comparable medical corpora. In Proc. Workshop on Building and Using Comparable Corpora. pages 2–10. Devlin, Siobhan and Gary Unthank. 2006. Helping aphasic people process online information. In Proc. ASSETS. pages 225–226. Elhadad, Noemie and Komal Sutaria. 2007. Mining a lexicon of technical terms and lay equivalents. In Proc. ACL BioNLP Workshop. pages 49–56. Fellbaum, Christiane, editor. 1998. WordNet: An Electronic Database. MIT Press, Cambridge, MA. Huenerfauth, Matt, Lijun Feng, and No´ emie Elhadad. 2009. Comparing evaluation techniques for text readability software for adults with intellectual disabilities. In Proc. ASSETS. pages 3–10. Jonnalagadda, Siddhartha, Luis Tari, J o¨rg Hakenberg, Chitta Baral, and Graciela Gonzalez. 2009. Towards effective sentence simplification for automatic processing of biomedical text. In Proc. NAACL-HLT. pages 177–180. McCarthy, Diana and Roberto Navigli. 2007. Semeval-2007 task 10: English lexical substitution task. In Proc. SemEval. pages 48–53. Napoles, Courtney and Mark Dredze. 2010. Learning simple wikipedia: a cogitation in ascertaining abecedarian language. In Proc. of the NAACLHLT Workshop on Computational Linguistics and Writing. pages 42–50. Nelken, Rani and Stuart Shieber. 2006. Towards robust context-sensitive sentence alignment for monolingual corpora. In Proc. EACL. pages 161– 166. Siddharthan, Advaith. 2004. Syntactic simplification and text cohesion. Technical Report UCAMCL-TR-597, University of Cambridge, Computer Laboratory. Vickrey, David and Daphne Koller. 2008. Applying sentence simplification to the CoNLL-2008 shared task. In Proc. CoNLL. pages 268–272. Williams, Sandra and Ehud Reiter. 2005. Generating readable texts for readers with low basic skills. In Proc. ENLG. pages 127–132. Yatskar, Mark, Bo Pang, Cristian DanescuNiculescu-Mizil, and Lillian Lee. 2010. For the sake of simplicity: Unsupervised extraction of lexical simplifications from wikipedia. In Proc. NAACL-HLT. pages 365–368. 501