Name
Abstract | ||
|---|---|---|
In this paper, we introduce a knowledge-based method to disambiguate biomedical acronyms using second-order co-occurrence vectors. We create these vectors using information about a long-form obtained from the Unified Medical Language System and Medline. We evaluate this method on a dataset of 18 acronyms found in biomedical text. Our method achieves an overall accuracy of 89%. The results show that using second-order features provide a distinct representation of the long-form and potentially enhances automated disambiguation. |
Year | Venue | Keywords |
|---|---|---|
2011 | CoNLL | biomedical text,unified medical language system,second-order vector,overall accuracy,knowledge-based method,automated disambiguation,distinct representation,acronym disambiguation,biomedical acronym,second-order co-occurrence vector,second-order feature |
Field | DocType | Citations |
Acronym,Information retrieval,Computer science,Natural language processing,Artificial intelligence,MEDLINE,Unified Medical Language System | Conference | 6 |
PageRank | References | Authors |
0.58 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bridget T. McInnes | 1 | 280 | 23.66 |
| Ted Pedersen | 2 | 2738 | 220.47 |
| Ying Liu | 3 | 1417 | 91.19 |
| Sergey V. Pakhomov | 4 | 55 | 5.99 |
| G B Melton | 5 | 264 | 45.72 |