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Adapting Data Mining for German Named Entity Recognition

  • In the latest decades, machine learning approaches have been intensively experimented for natural language processing. Most of the time, systems rely on using statistics within the system, by analyzing texts at the token level and, for labelling tasks, categorizing each among possible classes. One may notice that previous symbolic approaches (e.g. transducers) where designed to delimit pieces of text. Our research team developped mXS, a system that aims at combining both approaches. It locates boundaries of entities by using sequential pattern mining and machine learning. This system, intially developped for French, has been adapted to German.

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Metadaten
Author:Damien Nouvel, Jean-Yves Antoine
URN:https://nbn-resolving.org/urn:nbn:de:gbv:hil2-opus-3095
ISBN:978-3-934105-47-8
Document Type:Conference Proceeding
Language:English
Date of Publication (online):2014/11/25
Release Date:2014/11/25
Tag:NER; Named entity recognition
GND Keyword:Computerlinguistik
Source:Workshop Proceedings of the 12th KONVENS 2014
PPN:Link zum Katalog
Institutes:Fachbereich III / Informationswissenschaft und Sprachtechnologie
DDC classes:400 Sprache / 400 Sprache, Linguistik
Collections:KONVENS 2014 / Workshop Proceedings of the 12th KONVENS 2014
Licence (German):License LogoCreative Commons - Namensnennung 3.0