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Semantic author name disambiguation with word embeddings

  • We present a supervised machine learning AND system which tackles semantic similarity between publication titles by means of word embeddings. Word embeddings are integrated as external components, which keeps the model small and efficient, while allowing for easy extensibility and domain adaptation. Initial experiments show that word embeddings can improve the Recall and F score of the binary classification sub-task of AND. Results for the clustering sub-task are less clear, but also promising and overall show the feasibility of the approach.

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Metadaten
Author:Mark-Christoph MüllerORCiDGND
URN:urn:nbn:de:bsz:mh39-111355
DOI:https://doi.org/10.1007/978-3-319-67008-9_24
ISBN:978-3-319-67008-9
ISSN:1611-3349
Parent Title (English):Research and Advanced Technology for Digital Libraries. 21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017, Thessaloniki, Greece, September 18-21, 2017, Proceedings
Series (Serial Number):Lecture Notes in Computer Science (10450)
Publisher:Springer
Place of publication:Cham
Editor:Jaap Kamps, Giannis Tsakonas, Yannis Manolopoulos, Lazaros Iliadis, Ioannis Karydis
Document Type:Conference Proceeding
Language:English
Year of first Publication:2017
Date of Publication (online):2022/07/18
Publishing Institution:Leibniz-Institut für Deutsche Sprache (IDS) [Zweitveröffentlichung]
Publicationstate:Zweitveröffentlichung
Publicationstate:Postprint
Reviewstate:Peer-Review
Tag:author name disambiguation; classification; clustering; deep learning; machine learning; semantic similarity; word embeddings
GND Keyword:Computerlinguistik; Deep learning; Maschinelles Lernen; Semantik; Veröffentlichung
First Page:300
Last Page:311
DDC classes:400 Sprache / 400 Sprache, Linguistik
Open Access?:ja
Linguistics-Classification:Computerlinguistik
Licence (German):License LogoUrheberrechtlich geschützt