Multilingual Automatic Term Extraction in Low-Resource Domains
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 t...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2021-04-01
|
| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/128502 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849736487122239488 |
|---|---|
| author | NGOC TAN LE Fatiha Sadat |
| author_facet | NGOC TAN LE Fatiha Sadat |
| author_sort | NGOC TAN LE |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9552b2908c5d43a7b725ae9a62e54ee2 |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2021-04-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-9552b2908c5d43a7b725ae9a62e54ee22025-08-20T03:07:16ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622021-04-013410.32473/flairs.v34i1.12850262895Multilingual Automatic Term Extraction in Low-Resource DomainsNGOC TAN LE0Fatiha Sadat1Universite du Quebec a MontrealUniversite du Quebec a MontrealWith 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.https://journals.flvc.org/FLAIRS/article/view/128502 |
| spellingShingle | NGOC TAN LE Fatiha Sadat Multilingual Automatic Term Extraction in Low-Resource Domains Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| title | Multilingual Automatic Term Extraction in Low-Resource Domains |
| title_full | Multilingual Automatic Term Extraction in Low-Resource Domains |
| title_fullStr | Multilingual Automatic Term Extraction in Low-Resource Domains |
| title_full_unstemmed | Multilingual Automatic Term Extraction in Low-Resource Domains |
| title_short | Multilingual Automatic Term Extraction in Low-Resource Domains |
| title_sort | multilingual automatic term extraction in low resource domains |
| url | https://journals.flvc.org/FLAIRS/article/view/128502 |
| work_keys_str_mv | AT ngoctanle multilingualautomatictermextractioninlowresourcedomains AT fatihasadat multilingualautomatictermextractioninlowresourcedomains |