@inproceedings{lee-etal-2017-automatic,
title = "Automatic Difficulty Assessment for {C}hinese Texts",
author = "Lee, John and
Liu, Meichun and
Lam, Chun Yin and
Lau, Tak On and
Li, Bing and
Li, Keying",
editor = "Park, Seong-Bae and
Supnithi, Thepchai",
booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
month = nov,
year = "2017",
address = "Tapei, Taiwan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/I17-3012",
pages = "45--48",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Automatic Difficulty Assessment for Chinese Texts
%A Lee, John
%A Liu, Meichun
%A Lam, Chun Yin
%A Lau, Tak On
%A Li, Bing
%A Li, Keying
%Y Park, Seong-Bae
%Y Supnithi, Thepchai
%S Proceedings of the IJCNLP 2017, System Demonstrations
%D 2017
%8 November
%I Association for Computational Linguistics
%C Tapei, Taiwan
%F lee-etal-2017-automatic
%X 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.
%U https://aclanthology.org/I17-3012
%P 45-48
Markdown (Informal)
[Automatic Difficulty Assessment for Chinese Texts](https://aclanthology.org/I17-3012) (Lee et al., IJCNLP 2017)
ACL
- John Lee, Meichun Liu, Chun Yin Lam, Tak On Lau, Bing Li, and Keying Li. 2017. Automatic Difficulty Assessment for Chinese Texts. In Proceedings of the IJCNLP 2017, System Demonstrations, pages 45–48, Tapei, Taiwan. Association for Computational Linguistics.