  In this work, we present an approach based on combining string kernels and
word embeddings for automatic essay scoring. String kernels capture the
similarity among strings based on counting common character n-grams, which are
a low-level yet powerful type of feature, demonstrating state-of-the-art
results in various text classification tasks such as Arabic dialect
identification or native language identification. To our best knowledge, we are
the first to apply string kernels to automatically score essays. We are also
the first to combine them with a high-level semantic feature representation,
namely the bag-of-super-word-embeddings. We report the best performance on the
Automated Student Assessment Prize data set, in both in-domain and cross-domain
settings, surpassing recent state-of-the-art deep learning approaches.
