@inproceedings{cozma-etal-2018-automated,
title = "Automated essay scoring with string kernels and word embeddings",
author = "Cozma, M{\u{a}}d{\u{a}}lina and
Butnaru, Andrei and
Ionescu, Radu Tudor",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-2080",
doi = "10.18653/v1/P18-2080",
pages = "503--509",
abstract = "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.",
}
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%0 Conference Proceedings
%T Automated essay scoring with string kernels and word embeddings
%A Cozma, Mădălina
%A Butnaru, Andrei
%A Ionescu, Radu Tudor
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F cozma-etal-2018-automated
%X 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.
%R 10.18653/v1/P18-2080
%U https://aclanthology.org/P18-2080
%U https://doi.org/10.18653/v1/P18-2080
%P 503-509
Markdown (Informal)
[Automated essay scoring with string kernels and word embeddings](https://aclanthology.org/P18-2080) (Cozma et al., ACL 2018)
ACL
- Mădălina Cozma, Andrei Butnaru, and Radu Tudor Ionescu. 2018. Automated essay scoring with string kernels and word embeddings. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 503–509, Melbourne, Australia. Association for Computational Linguistics.