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GRAW+: A two‐view graph propagation method with word coupling for readability assessment
Author(s) -
Jiang Zhiwei,
Gu Qing,
Yin Yafeng,
Wang Jianxiang,
Chen Daoxu
Publication year - 2019
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.24123
Subject(s) - readability , computer science , graph , natural language processing , artificial intelligence , construct (python library) , word (group theory) , information retrieval , theoretical computer science , mathematics , geometry , programming language
Existing methods for readability assessment usually construct inductive classification models to assess the readability of singular text documents based on extracted features, which have been demonstrated to be effective. However, they rarely make use of the interrelationship among documents on readability, which can help increase the accuracy of readability assessment. In this article, we adopt a graph‐based classification method to model and utilize the relationship among documents using the coupled bag‐of‐words model. We propose a word coupling method to build the coupled bag‐of‐words model by estimating the correlation between words on reading difficulty. In addition, we propose a two‐view graph propagation method to make use of both the coupled bag‐of‐words model and the linguistic features. Our method employs a graph merging operation to combine graphs built according to different views, and improves the label propagation by incorporating the ordinal relation among reading levels. Experiments were conducted on both English and Chinese data sets, and the results demonstrate both effectiveness and potential of the method.