Karl Trela

How to find industry partners for public research? In our new #IEEE paper @yuricampbll, Friedrich Dornbusch, Anna Pohle and I present the classification approach behind our tool Corporate-Match, which helps 70 @Fraunhofer institutes with that. Paper: ieeexplore.ieee.org/document/91126… 1/5

📢We are excited to announce our paper " Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings" has been accepted to #EACL2024 at the @nlp4hr workshop. In cooperation with @NLPnorth and @MaiNLPlab.

@innogeo @EU2020DE Das Ding heißt Möbius-Schleife. Wieder was gelernt. twitter.com/BMBF_Bund/stat…

What a great result of one summer seminar with students: an entire book. Wish my seminars had been that interesting and productive back then.

I think every Data Scientist has noticed at some point how important and sometimes difficult it is to make your #MachineLearning model interpetable. This looks like an awesome resource with state of the art ways to do it. Will surely check it out. Thanks @ChristophMolnar!

We actually use a variant of this classification approach for our Corporate-Match prototype and our colleagues at @Fraunhofer are quite satisfied with the results. We are still working on improvements and would love to apply it to different organizations in future. 5/5

Turns out the combination of econometric data, geographic company-institute distance and WZ-industry-codes (German equivalent to NACE) performs the best. Texts which describe the industry branches instead of codes perform slightly worse on average, but with less variance. 4/5

How to find industry partners for public research? In our new #IEEE paper @yuricampbll, Friedrich Dornbusch, Anna Pohle and I present the classification approach behind our tool Corporate-Match, which helps 70 @Fraunhofer institutes with that. Paper: ieeexplore.ieee.org/document/91126… 1/5

I really enjoyed discussing our poster at #mlprague together with @algorithm87, on how we try find the right industry partners for our research institutes @Fraunhofer using #MachineLearning methods like text-classification and hybrid recommender systems. Thanks for dropping by!

Interesting future challenge for machine learning put forward by MITs Tomaso Poggio: " to learn like children"...with just a few labeled observations, possibly wit a lot of unlabeled data. Children do not need a million of pictures to learn how a car looks like. #mlprague

Wow, what a way to start a conference with playing this organ! #mlprague