|
When shallow is good enough: Automatic assessment of conceptual text complexity using shallow semantic features
Štajner, Sanja
;
Hulpus, Ioana
|
URL:
|
https://madoc.bib.uni-mannheim.de/55729
|
|
Weitere URL:
|
https://www.aclweb.org/anthology/2020.lrec-1.177/
|
|
URN:
|
urn:nbn:de:bsz:180-madoc-557291
|
|
Dokumenttyp:
|
Konferenzveröffentlichung
|
|
Erscheinungsjahr:
|
2020
|
|
Buchtitel:
|
LREC 2020 Marseille : Twelfth International Conference on Language Resources and Evaluation : May 11-16, 2020, Palais du Pharo, Marseille, France : conference proceedings
|
|
Seitenbereich:
|
1414-1422
|
|
Veranstaltungstitel:
|
LREC 2020 (conference canceled)
|
|
Veranstaltungsort:
|
Marseille, France
|
|
Veranstaltungsdatum:
|
canceled
|
|
Herausgeber:
|
Calzolari, Nicoletta
|
|
Ort der Veröffentlichung:
|
Paris
|
|
Verlag:
|
European Language Resources Association, ELRA-ELDA
|
|
ISBN:
|
979-10-95546-34-4
|
|
Verwandte URLs:
|
|
|
Sprache der Veröffentlichung:
|
Englisch
|
|
Einrichtung:
|
Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
|
|
Bereits vorhandene Lizenz:
|
 Creative Commons Namensnennung, nicht kommerziell 4.0 International (CC BY-NC 4.0)
|
|
Fachgebiet:
|
020 Bibliotheks- und Informationswissenschaft
|
|
Abstract:
|
According to psycholinguistic studies, the complexity of concepts used in a text and the relations between mentioned concepts play the most important role in text understanding and maintaining reader’s interest. However, the classical approaches to automatic assessment of text complexity, and their commercial applications, take into consideration mainly syntactic and lexical complexity. Recently, we introduced the task of automatic assessment of conceptual text complexity, proposing a set of graph-based deep semantic features using DBpedia as a proxy to human knowledge. Given that such graphs can be noisy, incomplete, and computationally expensive to deal with, in this paper, we propose the use of textual features and shallow semantic features that only require entity linking. We compare the results obtained with new features with those of the state-of-the-art deep semantic features on two tasks: (1) pairwise comparison of two versions of the same text; and (2) five-level classification of texts. We find that the shallow features achieve state-of-the-art results on both tasks, significantly outperforming performances of the deep semantic features on the five-level classification task. Interestingly, the combination of the shallow and deep semantic features lead to a significant improvement of the performances on that task.
|
 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 | Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt. |
 Suche Autoren in
Sie haben einen Fehler gefunden? Teilen Sie uns Ihren Korrekturwunsch bitte hier mit: E-Mail
Actions (login required)
 |
Eintrag anzeigen |
|
|
![[img]](https://madoc.bib.uni-mannheim.de/style/images/fileicons/application_pdf.png)