UniMa at SemEval-2018 Task 7 : semantic relation extraction and classification from scientific publications

Keiper, Thorsten ; Lyu, Zhonghao ; Pooladzadeh, Sara ; Xu, Yuan ; Zhang, Jingyi ; Lauscher, Anne ; Ponzetto, Simone Paolo

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DOI: https://doi.org/10.18653/v1/S18-1132
URL: https://madoc.bib.uni-mannheim.de/45623
Additional URL: http://aclweb.org/anthology/S18-1132
URN: urn:nbn:de:bsz:180-madoc-456230
Document Type: Conference or workshop publication
Year of publication: 2018
Book title: The International Workshop on Semantic Evaluation - proceedings of the twelfth workshop : June 5-June 6, 2018, New Orleans, Louisiana : NAACL HLT 2018
Page range: 826-830
Conference title: SemEval 2018
Location of the conference venue: New Orleans, LA
Date of the conference: June 5-6, 2018
Publisher: Apidianaki, Marianna
Place of publication: Stroudsburg, PA
Publishing house: Association for Computational Linguistics
ISBN: 978-1-948087-20-9
Publication language: English
Institution: School of Business Informatics and Mathematics > Information Systems III: Enterprise Data Analysis (Ponzetto 2016-)
School of Business Informatics and Mathematics > Text Analytics for Interdisciplinary Research (Juniorprofessur) (Glavaš 2017-2021)
License: CC BY 4.0 Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 004 Computer science, internet
Keywords (English): scientific publication mining , natural language processing , relation extraction , relation classification , deep learning
Abstract: Large repositories of scientific literature call for the development of robust methods to extract information from scholarly papers. This problem is addressed by the SemEval 2018 Task 7 on extracting and classifying relations found within scientific publications. In this paper, we present a feature-based and a deep learning-based approach to the task and discuss the results of the system runs that we submitted for evaluation.

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