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

Automated essay scoring with string kernels and word embeddings