We consider two state-of-the-art CWI systems: (i) the nearest centroid classifier proposed in (Yimam et al., 2017), which uses phrase length, number of senses, POS tags, word2vec cosine similarities, ngram frequency in Simple Wikipedia corpus and Google 1T corpus as features; and (ii) SV000gg (Paetzold and Specia, 2016b) which is an ensemble of binary classifiers trained with a combination of lexical, morphological, collocational, and semantic features.
these assumptions are not always accurate and are often the major source of errors in the simplification pipeline (Shardlow, 2014).