TY - GEN
T1 - ECNUICA at SemEval-2021 Task 11
T2 - 15th International Workshop on Semantic Evaluation, SemEval 2021, co-located with The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021
AU - Lin, Jiaju
AU - Ling, Jing
AU - Wang, Zhiwei
AU - Liu, Jiawei
AU - Chen, Qin
AU - He, Liang
N1 - Publisher Copyright:
© 2021 Association for Computational Linguistics.
PY - 2021
Y1 - 2021
N2 - This paper presents our endeavor for solving task11, NLPContributionGraph, of SemEval-2021. The purpose of the task is to extract triples from a paper in the Nature Language Processing field for constructing an Open Research Knowledge Graph. The task includes three sub-tasks: detecting the contribution sentences in papers, identifying scientific terms and predicate phrases from the contribution sentences; and inferring triples in the form of (subject, predicate, object) as statements for Knowledge Graph building. In this paper, we apply an ensemble of various fine-tuned pretrained language models (PLM) for tasks one and two. In addition, the self-training methods are adopted for tackling the shortage of annotated data. For the third task, rather than using classic neural open information extraction (OIE) architectures, we generate potential triples via manually designed rules and develop a binary classifier to differentiate positive ones from others. The quantitative results show that we obtain the 4th, 2nd, and 2nd rank in three evaluation phases.
AB - This paper presents our endeavor for solving task11, NLPContributionGraph, of SemEval-2021. The purpose of the task is to extract triples from a paper in the Nature Language Processing field for constructing an Open Research Knowledge Graph. The task includes three sub-tasks: detecting the contribution sentences in papers, identifying scientific terms and predicate phrases from the contribution sentences; and inferring triples in the form of (subject, predicate, object) as statements for Knowledge Graph building. In this paper, we apply an ensemble of various fine-tuned pretrained language models (PLM) for tasks one and two. In addition, the self-training methods are adopted for tackling the shortage of annotated data. For the third task, rather than using classic neural open information extraction (OIE) architectures, we generate potential triples via manually designed rules and develop a binary classifier to differentiate positive ones from others. The quantitative results show that we obtain the 4th, 2nd, and 2nd rank in three evaluation phases.
UR - https://www.scopus.com/pages/publications/85138867527
U2 - 10.18653/v1/2021.semeval-1.185
DO - 10.18653/v1/2021.semeval-1.185
M3 - 会议稿件
AN - SCOPUS:85138867527
T3 - SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop
SP - 1295
EP - 1302
BT - SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop
A2 - Palmer, Alexis
A2 - Schneider, Nathan
A2 - Schluter, Natalie
A2 - Emerson, Guy
A2 - Herbelot, Aurelie
A2 - Zhu, Xiaodan
PB - Association for Computational Linguistics (ACL)
Y2 - 5 August 2021 through 6 August 2021
ER -