@inproceedings{wang-etal-2023-corec,
title = "{C}o{R}ec: An Easy Approach for Coordination Recognition",
author = "Wang, Qing and
Jia, Haojie and
Song, Wenfei and
Li, Qi",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.934",
doi = "10.18653/v1/2023.emnlp-main.934",
pages = "15112--15120",
abstract = "In this paper, we observe and address the challenges of the coordination recognition task. Most existing methods rely on syntactic parsers to identify the coordinators in a sentence and detect the coordination boundaries. However, state-of-the-art syntactic parsers are slow and suffer from errors, especially for long and complicated sentences. To better solve the problems, we propose a pipeline model COordination RECognizer (CoRec). It consists of two components: coordinator identifier and conjunct boundary detector. The experimental results on datasets from various domains demonstrate the effectiveness and efficiency of the proposed method. Further experiments show that CoRec positively impacts downstream tasks, improving the yield of state-of-the-art Open IE models.",
}
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<abstract>In this paper, we observe and address the challenges of the coordination recognition task. Most existing methods rely on syntactic parsers to identify the coordinators in a sentence and detect the coordination boundaries. However, state-of-the-art syntactic parsers are slow and suffer from errors, especially for long and complicated sentences. To better solve the problems, we propose a pipeline model COordination RECognizer (CoRec). It consists of two components: coordinator identifier and conjunct boundary detector. The experimental results on datasets from various domains demonstrate the effectiveness and efficiency of the proposed method. Further experiments show that CoRec positively impacts downstream tasks, improving the yield of state-of-the-art Open IE models.</abstract>
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%0 Conference Proceedings
%T CoRec: An Easy Approach for Coordination Recognition
%A Wang, Qing
%A Jia, Haojie
%A Song, Wenfei
%A Li, Qi
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wang-etal-2023-corec
%X In this paper, we observe and address the challenges of the coordination recognition task. Most existing methods rely on syntactic parsers to identify the coordinators in a sentence and detect the coordination boundaries. However, state-of-the-art syntactic parsers are slow and suffer from errors, especially for long and complicated sentences. To better solve the problems, we propose a pipeline model COordination RECognizer (CoRec). It consists of two components: coordinator identifier and conjunct boundary detector. The experimental results on datasets from various domains demonstrate the effectiveness and efficiency of the proposed method. Further experiments show that CoRec positively impacts downstream tasks, improving the yield of state-of-the-art Open IE models.
%R 10.18653/v1/2023.emnlp-main.934
%U https://aclanthology.org/2023.emnlp-main.934
%U https://doi.org/10.18653/v1/2023.emnlp-main.934
%P 15112-15120
Markdown (Informal)
[CoRec: An Easy Approach for Coordination Recognition](https://aclanthology.org/2023.emnlp-main.934) (Wang et al., EMNLP 2023)
ACL
- Qing Wang, Haojie Jia, Wenfei Song, and Qi Li. 2023. CoRec: An Easy Approach for Coordination Recognition. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15112–15120, Singapore. Association for Computational Linguistics.