@inproceedings{howcroft-demberg-2017-psycholinguistic,
title = "Psycholinguistic Models of Sentence Processing Improve Sentence Readability Ranking",
author = "Howcroft, David M. and
Demberg, Vera",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-1090",
pages = "958--968",
abstract = "While previous research on readability has typically focused on document-level measures, recent work in areas such as natural language generation has pointed out the need of sentence-level readability measures. Much of psycholinguistics has focused for many years on processing measures that provide difficulty estimates on a word-by-word basis. However, these psycholinguistic measures have not yet been tested on sentence readability ranking tasks. In this paper, we use four psycholinguistic measures: idea density, surprisal, integration cost, and embedding depth to test whether these features are predictive of readability levels. We find that psycholinguistic features significantly improve performance by up to 3 percentage points over a standard document-level readability metric baseline.",
}
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%0 Conference Proceedings
%T Psycholinguistic Models of Sentence Processing Improve Sentence Readability Ranking
%A Howcroft, David M.
%A Demberg, Vera
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F howcroft-demberg-2017-psycholinguistic
%X While previous research on readability has typically focused on document-level measures, recent work in areas such as natural language generation has pointed out the need of sentence-level readability measures. Much of psycholinguistics has focused for many years on processing measures that provide difficulty estimates on a word-by-word basis. However, these psycholinguistic measures have not yet been tested on sentence readability ranking tasks. In this paper, we use four psycholinguistic measures: idea density, surprisal, integration cost, and embedding depth to test whether these features are predictive of readability levels. We find that psycholinguistic features significantly improve performance by up to 3 percentage points over a standard document-level readability metric baseline.
%U https://aclanthology.org/E17-1090
%P 958-968
Markdown (Informal)
[Psycholinguistic Models of Sentence Processing Improve Sentence Readability Ranking](https://aclanthology.org/E17-1090) (Howcroft & Demberg, EACL 2017)
ACL