Abstract
We present a text simplifier for English that has been built with open source software and has both lexical and syntactic simplification capabilities. The lexical simplifier uses a vector space model approach to obtain the most appropriate sense of a given word in a given context and word frequency simplicity measures to rank synonyms. The syntactic simplifier uses linguistically-motivated rule-based syntactic analysis and generation techniques that rely on part-of-speech tags and syntactic dependency information. Experimental results show good performance of the lexical simplification component when compared to a hard-to-beat baseline, good syntactic simplification accuracy, and according to human assessment, improvements over the best reported results in the literature for a system with same architecture as YATS.
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simplewiki-20140204 dump version.
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It uses the Mate Tools’ PoS tagger and lemmatizer before parsing.
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None of them developed the simplifier.
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Acknowledgments
We are grateful to three anonymous reviewers for their useful comments, to the participants in our human evaluation experiments, and to A. Siddharthan for sharing his dataset. This work was funded by the ABLE-TO-INCLUDE project (European Commission CIP Grant No. 621055). Horacio Saggion is (partly) supported by the Spanish MINECO Ministry (MDM-2015-0502).
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Ferrés, D., Marimon, M., Saggion, H., AbuRa’ed, A. (2016). YATS: Yet Another Text Simplifier. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2016. Lecture Notes in Computer Science(), vol 9612. Springer, Cham. https://doi.org/10.1007/978-3-319-41754-7_32
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