Applying Natural Language Processing in Manufacturing

Author:

May Marvin Carl,Neidhöfer Jan,Körner Tom,Schäfer Louis,Lanza Gisela

Funder

Horizon 2020

Horizon Europe

Publisher

Elsevier BV

Subject

General Medicine

Reference24 articles.

1. Technological troubleshooting based on sentence embedding with deep transformers;Alfeo;Journal of Intelligent Manufacturing,2021

2. Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D., 2020. Language models are few-shot learners. doi:10.48550/ARXIV.2005.14165.

3. Technical language processing: Unlocking maintenance knowledge;Brundage;Manufacturing Letters,2021

4. Xgboost: A scalable tree boosting system;Chen,2016

5. Ferryman at SemEval-2020 task 3: Bert with TFIDF-weighting for predicting the effect of context in word similarity;Chen,2020

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