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Date
05.10.2022 | 14:00 - 15:00 (CET)

AI-Café presents: Named Entity Recognition: Using deep-learning approaches for extracting entities

We will briefly introduce the deep-learning approaches that tackle the task of NER, followed by a comparative evaluation between multiple setups of a chosen network architecture. The biggest available datasets for German, CoNLL-2003 and GermEval2014 will be used to train and evaluate the NER models. Finally, we will see how transfer learning can be applied, to improve the performance of pre-trained models in "low-data" settings.

This Café is organized by Grassroots Arts. If you have questions about the organisation of this AI-Café or if you want to become a speaker yourself in one of the next Web Café Sessions, please contact carmen@grassroots-arts.eu.

The recordings of the past Web Cafés you can find on our AI-Café video channel: https://www.gotostage.com/channel/ai-cafe.

Here is the link to the AI-Café website: https://ai-cafe.eu/

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Archive of all AI4Media related AI-Cafés: https://www.ai4media.eu/ai-cafes/ 

Speakers

Andel Gugu

Speaker's short bio:

My name is Andel Gugu, and I work as a research engineer. I have a Master’s Degree in Computer Science from the University of Bonn, where the main track of my studies was ‘Intelligent Systems’. During my studies, I specialized in courses covering machine learning, artificial intelligence, and neural networks. My interest in these subjects aligned with that of Fraunhofer IAIS, where I started working as a student assistant in November 2019. During my work, I became familiar with different media analysis services and the technologies behind them. I wrote my thesis on ‘DL-based Entity Recognition models’ under the supervision of ‘The Media Analysis Platform’ team’s leader. Last year, I switched from being a student assistant to working as a full-time employee. I am now part of the team that develops and maintains the Mining Platform, but I also work on improving the quality of some of the existing media services provided by this platform.