MinScIE: Citation-centered open information extraction

Lauscher, Anne ; Song, Yide ; Gashteovski, Kiril

_JCDL19Demo__MinScIE (4).pdf - Published

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DOI: https://doi.org/10.1109/JCDL.2019.00083
URL: https://madoc.bib.uni-mannheim.de/49216
Additional URL: https://ieeexplore.ieee.org/abstract/document/8791...
URN: urn:nbn:de:bsz:180-madoc-492168
Document Type: Conference or workshop publication
Year of publication: 2019
Book title: 2019 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2019 : proceedings, 2-6 June 2019, Urbana-Champaign, Illinois
Page range: 386-387
Conference title: JCDL 2019
Location of the conference venue: Urbana-Champaign, IL
Date of the conference: June 2-6, 2019
Publisher: Bonn, Maria
Place of publication: New York, NY
Publishing house: ACM Press
ISBN: 978-1-7281-1548-1 , 978-1-7281-1547-4
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)
Subject: 004 Computer science, internet
Keywords (English): Information Extraction; Scientific Publication Mining; Classification; Natural Language Processing; Machine Learning
Abstract: Acknowledging the importance of citations in scientific literature, in this work we present MinScIE, an Open Information Extraction system which provides structured knowledge enriched with semantic information about citations. By comparing our system to it’s original core, MinIE, we show that our approach improves extraction precision by 3 percentage points.

Dieser Eintrag ist Teil der Universitätsbibliographie.

Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt.

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