Comunicació presentada a la 21st International Conference on Applications of Natural Language to Information Systems (NLDB 2016), celebrada els dies 22 a 24 de juny de 2016 a Salford, Regne Unit.

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.

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).