@inproceedings{lin-etal-2024-automatic,
title = "Automatic Construction of the {E}nglish Sentence Pattern Structure Treebank for {C}hinese {ESL} learners",
author = "Zhu, Lin and
Xu, Meng and
Guo, Wenya and
Yu, Jingsi and
Yang, Liner and
Cao, Zehuang and
Huang, Yuan and
Yang, Erhong",
editor = "Maosong, Sun and
Jiye, Liang and
Xianpei, Han and
Zhiyuan, Liu and
Yulan, He",
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 = "``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.''"
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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 Zhu, Lin
%A Xu, Meng
%A Guo, Wenya
%A Yu, Jingsi
%A Yang, Liner
%A Cao, Zehuang
%A Huang, Yuan
%A Yang, Erhong
%Y Maosong, Sun
%Y Jiye, Liang
%Y Xianpei, Han
%Y Zhiyuan, Liu
%Y Yulan, He
%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/) (Zhu et al., CCL 2024)
ACL
- Lin Zhu, Meng Xu, Wenya Guo, Jingsi Yu, Liner Yang, Zehuang Cao, Yuan Huang, and Erhong Yang. 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.