@inproceedings{yang-etal-2017-alibaba,
title = "{A}libaba at {IJCNLP}-2017 Task 1: Embedding Grammatical Features into {LSTM}s for {C}hinese Grammatical Error Diagnosis Task",
author = "Yang, Yi and
Xie, Pengjun and
Tao, Jun and
Xu, Guangwei and
Li, Linlin and
Si, Luo",
editor = "Liu, Chao-Hong and
Nakov, Preslav and
Xue, Nianwen",
booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
month = dec,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/I17-4006",
pages = "41--46",
abstract = "This paper introduces Alibaba NLP team system on IJCNLP 2017 shared task No. 1 Chinese Grammatical Error Diagnosis (CGED). The task is to diagnose four types of grammatical errors which are redundant words (R), missing words (M), bad word selection (S) and disordered words (W). We treat the task as a sequence tagging problem and design some handcraft features to solve it. Our system is mainly based on the LSTM-CRF model and 3 ensemble strategies are applied to improve the performance. At the identification level and the position level our system gets the highest F1 scores. At the position level, which is the most difficult level, we perform best on all metrics.",
}
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<abstract>This paper introduces Alibaba NLP team system on IJCNLP 2017 shared task No. 1 Chinese Grammatical Error Diagnosis (CGED). The task is to diagnose four types of grammatical errors which are redundant words (R), missing words (M), bad word selection (S) and disordered words (W). We treat the task as a sequence tagging problem and design some handcraft features to solve it. Our system is mainly based on the LSTM-CRF model and 3 ensemble strategies are applied to improve the performance. At the identification level and the position level our system gets the highest F1 scores. At the position level, which is the most difficult level, we perform best on all metrics.</abstract>
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%0 Conference Proceedings
%T Alibaba at IJCNLP-2017 Task 1: Embedding Grammatical Features into LSTMs for Chinese Grammatical Error Diagnosis Task
%A Yang, Yi
%A Xie, Pengjun
%A Tao, Jun
%A Xu, Guangwei
%A Li, Linlin
%A Si, Luo
%Y Liu, Chao-Hong
%Y Nakov, Preslav
%Y Xue, Nianwen
%S Proceedings of the IJCNLP 2017, Shared Tasks
%D 2017
%8 December
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F yang-etal-2017-alibaba
%X This paper introduces Alibaba NLP team system on IJCNLP 2017 shared task No. 1 Chinese Grammatical Error Diagnosis (CGED). The task is to diagnose four types of grammatical errors which are redundant words (R), missing words (M), bad word selection (S) and disordered words (W). We treat the task as a sequence tagging problem and design some handcraft features to solve it. Our system is mainly based on the LSTM-CRF model and 3 ensemble strategies are applied to improve the performance. At the identification level and the position level our system gets the highest F1 scores. At the position level, which is the most difficult level, we perform best on all metrics.
%U https://aclanthology.org/I17-4006
%P 41-46
Markdown (Informal)
[Alibaba at IJCNLP-2017 Task 1: Embedding Grammatical Features into LSTMs for Chinese Grammatical Error Diagnosis Task](https://aclanthology.org/I17-4006) (Yang et al., IJCNLP 2017)
ACL