TY - GEN
T1 - A constituency parsing tree based method for relation extraction from abstracts of scholarly publications
AU - Jiang, Ming
AU - Diesner, Jana
N1 - Funding Information:
This material is based upon work supported by the U.S. Department of Homeland Security under Grant Award Number, 2015-ST-061-CIRC01. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security.
Publisher Copyright:
© 2019 EMNLP-IJCNLP 2019 - Graph-Based Methods for Natural Language Processing - Proceedings of the 13th Workshop. All rights reserved.
PY - 2019
Y1 - 2019
N2 - We present a simple, rule-based method for extracting entity networks from the abstracts of scientific literature. By taking advantage of selected syntactic features of constituent parsing trees, our method automatically extracts and constructs graphs in which nodes represent text-based entities (in this case, noun phrases) and their relationships (in this case, verb phrases or preposition phrases). We use two benchmark datasets for evaluation and compare with previously presented results for these data . Our evaluation results show that the proposed method leads to accuracy rates that are comparable to or exceed the results achieved with state-of-the-art, learning-based methods in several cases.
AB - We present a simple, rule-based method for extracting entity networks from the abstracts of scientific literature. By taking advantage of selected syntactic features of constituent parsing trees, our method automatically extracts and constructs graphs in which nodes represent text-based entities (in this case, noun phrases) and their relationships (in this case, verb phrases or preposition phrases). We use two benchmark datasets for evaluation and compare with previously presented results for these data . Our evaluation results show that the proposed method leads to accuracy rates that are comparable to or exceed the results achieved with state-of-the-art, learning-based methods in several cases.
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M3 - Conference contribution
AN - SCOPUS:85085052925
T3 - EMNLP-IJCNLP 2019 - Graph-Based Methods for Natural Language Processing - Proceedings of the 13th Workshop
SP - 186
EP - 191
BT - EMNLP-IJCNLP 2019 - Graph-Based Methods for Natural Language Processing - Proceedings of the 13th Workshop
PB - Association for Computational Linguistics (ACL)
T2 - 13th Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs 2019, in conjunction with the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019
Y2 - 4 November 2019 through 4 November 2019
ER -