Goran Nenadic

Computer Science

1. Text Mining 100%
2. Natural Language Processing 38%
3. Biomedical Literature 34%
4. Machine Learning 32%
5. Annotation 23%
6. Ontology 19%
7. Information Extraction 19%
8. Machine Learning Approach 19%
9. Case Study 18%
10. Pre-Trained Language Models 17%
11. Syntactics 15%
12. Social Media 15%
13. Deep Learning 15%
14. Scientific Literature 15%
15. Named Entity Recognition 14%
16. Common Practice 13%
17. Machine Translation 13%
18. Support Vector Machine 13%
19. Protected Health Information 12%
20. Word Embedding 11%
21. Gold Standard 11%
22. Conditional Random Field 10%
23. Training Dataset 10%
24. Extract Information 9%
25. Training Data 9%
26. Transfer Learning 9%
27. Long Short-Term Memory Network 9%
28. Semantic Network 9%
29. Bidirectional Long Short-Term Memory Network 9%
30. Data Curation 9%
31. Condition Monitoring 9%
32. Data Analytics 9%
33. Text Analytics 9%
34. Data Source 9%
35. Decision-Making 8%
36. Experimental Result 8%
37. Temporal Information 7%
38. Related Resource 7%
39. Clinical Data 7%
40. Feature Selection 7%
41. Contextual Information 7%
42. Research Purpose 7%
43. Large Language Model 7%
44. Convolutional Neural Network 6%
45. Bidirectional Encoder Representations From Transformers 6%
46. Hybrid Approach 6%
47. Structured Data 6%
48. Feature Space 6%
49. Good Performance 6%
50. Software Development 6%