Semi-Supervised Joint Estimation of Word and Document Readability

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10442310" target="_blank" >RIV/00216208:11320/21:10442310 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Semi-Supervised Joint Estimation of Word and Document Readability

Original language description

Readability or difficulty estimation of words and documents has been investigated independently in the literature, often assuming the existence of extensive annotated resources for the other. Motivated by our analysis showing that there is a recursive relationship between word and document difficulty, we propose to jointly estimate word and document difficulty through a graph convolutional network (GCN) in a semi-supervised fashion. Our experimental results reveal that the GCN-based method can achieve higher accuracy than strong baselines, and stays robust even with a smaller amount of labeled data.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)

ISBN

978-1-954085-38-1

ISSN

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e-ISSN

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Number of pages

6

Pages from-to

150-155

Publisher name

Association for Computational Linguistics

Place of publication

Stroudsburg

Event location

Mexico City

Event date

Jun 11, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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