A Constituency Parsing Tree based Method for Relation Extraction from Abstracts of Scholarly Publications

Ming Jiang, Jana Diesner


Abstract
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.
Anthology ID:
D19-5323
Volume:
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
Month:
November
Year:
2019
Address:
Hong Kong
Venues:
EMNLP | TextGraphs | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
186–191
Language:
URL:
https://aclanthology.org/D19-5323
DOI:
10.18653/v1/D19-5323
Bibkey:
Cite (ACL):
Ming Jiang and Jana Diesner. 2019. A Constituency Parsing Tree based Method for Relation Extraction from Abstracts of Scholarly Publications. In Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13), pages 186–191, Hong Kong. Association for Computational Linguistics.
Cite (Informal):
A Constituency Parsing Tree based Method for Relation Extraction from Abstracts of Scholarly Publications (Jiang & Diesner, EMNLP 2019)
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PDF:
https://aclanthology.org/D19-5323.pdf