@inproceedings{lin-etal-2024-automatic,
title = "Automatic Construction of the {E}nglish Sentence Pattern Structure Treebank for {C}hinese {ESL} learners",
author = "Lin, Zhu and
Meng, Xu and
Wenya, Guo and
Jingsi, Yu and
Liner, Yang and
Zehuang, Cao and
Yuan, Huang and
Erhong, Yang",
editor = "Sun, Maosong and
Liang, Jiye and
Han, Xianpei and
Liu, Zhiyuan and
He, Yulan",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-1.95/",
pages = "1223--1238",
language = "eng",
abstract = "{\textquotedblleft}Analyzing long and complicated sentences has always been a priority and challenge in Englishlearning. In order to conduct the parse of these sentences for Chinese English as Second Lan-guage (ESL) learners, we design the English Sentence Pattern Structure (ESPS) based on theSentence Diagramming theory. Then, we automatically construct the English Sentence PatternStructure Treebank (ESPST) through the method of rule conversion based on constituency struc-ture and evaluate the conversion results. In addition, we set up two comparative experiments,using trained parser and large language models (LLMs). The results prove that the rule-basedconversion approach is effective.{\textquotedblright}"
}
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<abstract>“Analyzing long and complicated sentences has always been a priority and challenge in Englishlearning. In order to conduct the parse of these sentences for Chinese English as Second Lan-guage (ESL) learners, we design the English Sentence Pattern Structure (ESPS) based on theSentence Diagramming theory. Then, we automatically construct the English Sentence PatternStructure Treebank (ESPST) through the method of rule conversion based on constituency struc-ture and evaluate the conversion results. In addition, we set up two comparative experiments,using trained parser and large language models (LLMs). The results prove that the rule-basedconversion approach is effective.”</abstract>
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%0 Conference Proceedings
%T Automatic Construction of the English Sentence Pattern Structure Treebank for Chinese ESL learners
%A Lin, Zhu
%A Meng, Xu
%A Wenya, Guo
%A Jingsi, Yu
%A Liner, Yang
%A Zehuang, Cao
%A Yuan, Huang
%A Erhong, Yang
%Y Sun, Maosong
%Y Liang, Jiye
%Y Han, Xianpei
%Y Liu, Zhiyuan
%Y He, Yulan
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F lin-etal-2024-automatic
%X “Analyzing long and complicated sentences has always been a priority and challenge in Englishlearning. In order to conduct the parse of these sentences for Chinese English as Second Lan-guage (ESL) learners, we design the English Sentence Pattern Structure (ESPS) based on theSentence Diagramming theory. Then, we automatically construct the English Sentence PatternStructure Treebank (ESPST) through the method of rule conversion based on constituency struc-ture and evaluate the conversion results. In addition, we set up two comparative experiments,using trained parser and large language models (LLMs). The results prove that the rule-basedconversion approach is effective.”
%U https://aclanthology.org/2024.ccl-1.95/
%P 1223-1238
Markdown (Informal)
[Automatic Construction of the English Sentence Pattern Structure Treebank for Chinese ESL learners](https://aclanthology.org/2024.ccl-1.95/) (Lin et al., CCL 2024)
ACL
- Zhu Lin, Xu Meng, Guo Wenya, Yu Jingsi, Yang Liner, Cao Zehuang, Huang Yuan, and Yang Erhong. 2024. Automatic Construction of the English Sentence Pattern Structure Treebank for Chinese ESL learners. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference), pages 1223–1238, Taiyuan, China. Chinese Information Processing Society of China.