Multilingual Automatic Term Extraction in Low-Resource Domains

Authors

  • NGOC TAN LE Universite du Quebec a Montreal
  • Fatiha Sadat Universite du Quebec a Montreal

DOI:

https://doi.org/10.32473/flairs.v34i1.128502

Abstract

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.

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Published

2021-04-18

How to Cite

LE, N. T., & Sadat, F. (2021). Multilingual Automatic Term Extraction in Low-Resource Domains. The International FLAIRS Conference Proceedings, 34. https://doi.org/10.32473/flairs.v34i1.128502

Issue

Section

Special Track: Semantic, Logics, Information Extraction and AI