@inproceedings{zhao-etal-2019-context,
title = "A Context-based Framework for Modeling the Role and Function of On-line Resource Citations in Scientific Literature",
author = "Zhao, He and
Luo, Zhunchen and
Feng, Chong and
Zheng, Anqing and
Liu, Xiaopeng",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1524",
doi = "10.18653/v1/D19-1524",
pages = "5206--5215",
abstract = "We introduce a new task of modeling the role and function for on-line resource citations in scientific literature. By categorizing the on-line resources and analyzing the purpose of resource citations in scientific texts, it can greatly help resource search and recommendation systems to better understand and manage the scientific resources. For this novel task, we are the first to create an annotation scheme, which models the different granularity of information from a hierarchical perspective. And we construct a dataset SciRes, which includes 3,088 manually annotated resource contexts. In this paper, we propose a possible solution by using a multi-task framework to build the scientific resource classifier (SciResCLF) for jointly recognizing the role and function types. Then we use the classification results to help a scientific resource recommendation (SciResREC) task. Experiments show that our model achieves the best results on both the classification task and the recommendation task. The SciRes dataset is released for future research.",
}
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%0 Conference Proceedings
%T A Context-based Framework for Modeling the Role and Function of On-line Resource Citations in Scientific Literature
%A Zhao, He
%A Luo, Zhunchen
%A Feng, Chong
%A Zheng, Anqing
%A Liu, Xiaopeng
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F zhao-etal-2019-context
%X We introduce a new task of modeling the role and function for on-line resource citations in scientific literature. By categorizing the on-line resources and analyzing the purpose of resource citations in scientific texts, it can greatly help resource search and recommendation systems to better understand and manage the scientific resources. For this novel task, we are the first to create an annotation scheme, which models the different granularity of information from a hierarchical perspective. And we construct a dataset SciRes, which includes 3,088 manually annotated resource contexts. In this paper, we propose a possible solution by using a multi-task framework to build the scientific resource classifier (SciResCLF) for jointly recognizing the role and function types. Then we use the classification results to help a scientific resource recommendation (SciResREC) task. Experiments show that our model achieves the best results on both the classification task and the recommendation task. The SciRes dataset is released for future research.
%R 10.18653/v1/D19-1524
%U https://aclanthology.org/D19-1524
%U https://doi.org/10.18653/v1/D19-1524
%P 5206-5215
Markdown (Informal)
[A Context-based Framework for Modeling the Role and Function of On-line Resource Citations in Scientific Literature](https://aclanthology.org/D19-1524) (Zhao et al., EMNLP-IJCNLP 2019)
ACL