Within the BIOfid project, the pre-trained word embeddings for the recent publication at IEEE ICMLA 2018 [1] have been uploaded under the following link.
[1] ![[pdf]](https://www.texttechnologylab.org/wp-content/plugins/papercite/img/pdf.png)
S. Ahmed and A. Mehler, “Resource-Size matters: Improving Neural Named Entity Recognition with Optimized Large Corpora,” in Proceedings of the 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018.
[Bibtex]
S. Ahmed and A. Mehler, “Resource-Size matters: Improving Neural Named Entity Recognition with Optimized Large Corpora,” in Proceedings of the 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018. [Bibtex]
@InProceedings{Ahmed:Mehler:2018,
author = {Sajawel Ahmed and Alexander Mehler},
title = {{Resource-Size matters: Improving Neural Named Entity Recognition with Optimized Large Corpora}},
abstract = {This study improves the performance of neural named entity recognition by a margin of up to 11% in terms of F-score on the example of a low-resource language like German, thereby outperforming existing baselines and establishing a new state-of-the-art on each single open-source dataset (CoNLL 2003, GermEval 2014 and Tübingen Treebank 2018). Rather than designing deeper and wider hybrid neural architectures, we gather all available resources and perform a detailed optimization and grammar-dependent morphological processing consisting of lemmatization and part-of-speech tagging prior to exposing the raw data to any training process. We test our approach in a threefold monolingual experimental setup of a) single, b) joint, and c) optimized training and shed light on the dependency of downstream-tasks on the size of corpora used to compute word embeddings.},
booktitle = {Proceedings of the 17th IEEE International Conference on Machine Learning and Applications (ICMLA)},
location = {Orlando, Florida, USA},
pdf = {https://arxiv.org/pdf/1807.10675.pdf},
year = 2018
}