Name
Abstract | ||
|---|---|---|
Named Entity Recognition (NER) is an important task in biomedical NLP which identifies and categorizes entities in biomedical text. We currently focus on a rule-based approach for NER to identify the diagnostic criteria of valley fever in the free text of electronic health records (EHRs), since no training data exist for machine learning. To aid the manual pattern defining process of the rule-based approach, we propose a graph-based lexicon expansion method. We used different word embedding models to create a lexicon graph and expanded the lexicons by conducting different graph search methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CHASE52844.2021.00021 | 2021 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) |
Keywords | DocType | ISBN |
word embeddings,lexicon,graph search,lexicon expansion,lexicon extension,valley fever,electronic health records,EHR | Conference | 978-1-6654-3966-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyun-Ju Song | 1 | 0 | 0.34 |
| Yang Gu | 2 | 0 | 1.69 |
| Gondy Leroy | 3 | 0 | 1.01 |
| Fariba M. Donovan | 4 | 0 | 0.34 |
| John N. Galgiani | 5 | 0 | 0.34 |