Name
Title | ||
|---|---|---|
Do You Need Embeddings Trained on a Massive Specialized Corpus for Your Clinical Natural Language Processing Task? |
Abstract | ||
|---|---|---|
We explore the impact of data source on word representations for different NLP tasks in the clinical domain in French (natural language understanding and text classification). We compared word embeddings (Fasttext) and language models (ELMo), learned either on the general domain (Wikipedia) or on specialized data (electronic health records, EHR). The best results were obtained with ELMo representations learned on EHR data for one of the two tasks(+ 7% and +8% of gain in F1-score). |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3233/SHTI190533 | Studies in Health Technology and Informatics |
Keywords | Field | DocType |
Natural language processing,electronic health records | Natural language processing,Artificial intelligence,Medicine | Conference |
Volume | ISSN | Citations |
264 | 0926-9630 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antoine Neuraz | 1 | 16 | 4.22 |
| Vincent Looten | 2 | 0 | 0.34 |
| Bastien Rance | 3 | 65 | 11.91 |
| Nicolas Daniel | 4 | 0 | 0.34 |
| N Garcelon | 5 | 40 | 6.01 |
| leonardo campillos llanos | 6 | 9 | 8.39 |
| Anita Burgun | 7 | 506 | 57.91 |
| Sophie Rosset | 8 | 393 | 61.66 |