Automatic Difficulty Assessment for Chinese Texts

John Lee, Meichun Liu, Chun Yin Lam, Tak On Lau, Bing Li, Keying Li


Abstract
We present a web-based interface that automatically assesses reading difficulty of Chinese texts. The system performs word segmentation, part-of-speech tagging and dependency parsing on the input text, and then determines the difficulty levels of the vocabulary items and grammatical constructions in the text. Furthermore, the system highlights the words and phrases that must be simplified or re-written in order to conform to the user-specified target difficulty level. Evaluation results show that the system accurately identifies the vocabulary level of 89.9% of the words, and detects grammar points at 0.79 precision and 0.83 recall.
Anthology ID:
I17-3012
Volume:
Proceedings of the IJCNLP 2017, System Demonstrations
Month:
November
Year:
2017
Address:
Tapei, Taiwan
Venue:
IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
45–48
Language:
URL:
https://www.aclweb.org/anthology/I17-3012
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/I17-3012.pdf