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|Title:||Psycholinguistic Models of Sentence Processing Improve Sentence Readability Ranking|
|Author(s):||Howcroft, David M.|
Camilleri, John J.
Coll Ardanuy, Mariona
|Title:||European Chapter of the Association for Computational Linguistics - proceedings of the Student Research Workshop|
|Year of Publication:||2017|
|Place of publication:||Stroudsburg, PA|
|Title of the Conference:||EACL 2017|
|Place of the conference:||Valencia, Spain|
|Publikation type:||Conference Paper|
|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.|
|DOI of the first publication:||10.18653/v1/E17-1090|
|URL of the first publication:||https://www.aclweb.org/anthology/E17-1090/|
|Link to this record:||hdl:20.500.11880/29718|
|Date of registration:||23-Sep-2020|
|Faculty:||MI - Fakultät für Mathematik und Informatik|
|Department:||MI - Informatik|
|Professorship:||MI - Prof. Dr. Vera Demberg|
|Collections:||UniBib – Die Universitätsbibliographie|
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