EAL: A toolkit and dataset for entity-aspect linking

Nanni, Federico ; Zhang, Jingyi ; Betz, Ferdinand ; Gashteovski, Kiril

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DOI: https://doi.org/10.1109/JCDL.2019.00104
URL: https://madoc.bib.uni-mannheim.de/49596
Additional URL: https://ieeexplore.ieee.org/abstract/document/8791...
URN: urn:nbn:de:bsz:180-madoc-495963
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: 430-431
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
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-)
Außerfakultäre Einrichtungen > SFB 884
License: CC BY 4.0 Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 020 Library and information sciences
Abstract: We present a toolkit and dataset for entity-aspect linking. The tool takes as input a sentence and provides the most relevant aspect for each mentioned entity; it is implemented in Python and available as a script and via an online demo. It is accompanied by the first large dataset of entity-aspects, comprising more than 20,000 entities manually linked to the most relevant aspect, given a sentence as context. Each is expressed in structured manner as Open Information Extraction (OIE) triples (Subject, Relation, Object), having semantic information for polarity, modality, quantity and attributions.

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