Work

Feature Optimization for Predicting Readability of Arabic L1 and L2

Year: 2018

Type: conference-paper

Abstract: Advances in automatic readability assessment can impact the way people consume information in a number of domains.Arabic, being a low-resource and morphologically complex language, presents numerous challenges to the task of automatic readability assessment.In this paper, we present the largest and most in-depth computational readability study for Arabic to date.We study a large set of features with varying depths, from shallow words to syntactic trees, for both L1 and L2 readability tasks.Our best L1 readability accuracy result is 94.8% (75% error reduction from a commonly used baseline).The comparable results for L2 are 72.4% (45% error reduction).We also demonstrate the added value of leveraging L1 features for L2 readability prediction.Corpus Depth of Features LM Results Size (tokens) L1 L2 Raw Morph Syn Features Reported Al-Khalifa and Al-Ajlan (2010) 150 docs (57,089)

Source: Workshop on Natural Language Processing Techniques for Educational Applications (NLP-TEA)

DOI: 10.18653/v1/w18-3703

Authors: Hind Saddiki, Nizar Habash, Violetta Cavalli-Sforza, Muhamed Al Khalil

Institutions: New York University Abu Dhabi, Mohammed V University, Al Akhawayn University

Language: English

Topic: Text Readability and Simplification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W2885479598

FWCI: 0.714

Cites: 30

Cited by: 16

Related to: 10