@article{LE_Sadat_2021, title={Multilingual Automatic Term Extraction in Low-Resource Domains}, volume={34}, url={https://journals.flvc.org/FLAIRS/article/view/128502}, DOI={10.32473/flairs.v34i1.128502}, abstractNote={<p>With the emergence of the neural networks-based approaches, research on information extraction has benefited from large-scale raw texts by leveraging them using pre-trained embeddings and other data augmentation techniques to deal with challenges and issues in Natural Language Processing tasks. In this paper, we propose an approach using sequence-to-sequence neural networks-based models to deal with term extraction for low-resource domain. Our empirical experiments, evaluating on the multilingual ACTER dataset provided in the LREC-TermEval 2020 shared task on automatic term extraction, proved the efficiency of deep learning approach, in the case of low-data settings, for the automatic term extraction task.</p>}, journal={The International FLAIRS Conference Proceedings}, author={LE, NGOC TAN and Sadat, Fatiha}, year={2021}, month={Apr.} }