  In this paper, we present a kernel-based learning approach for the 2018
Complex Word Identification (CWI) Shared Task. Our approach is based on
combining multiple low-level features, such as character n-grams, with
high-level semantic features that are either automatically learned using word
embeddings or extracted from a lexical knowledge base, namely WordNet. After
feature extraction, we employ a kernel method for the learning phase. The
feature matrix is first transformed into a normalized kernel matrix. For the
binary classification task (simple versus complex), we employ Support Vector
Machines. For the regression task, in which we have to predict the complexity
level of a word (a word is more complex if it is labeled as complex by more
annotators), we employ v-Support Vector Regression. We applied our approach
only on the three English data sets containing documents from Wikipedia,
WikiNews and News domains. Our best result during the competition was the third
place on the English Wikipedia data set. However, in this paper, we also report
better post-competition results.
