Tagger is a named entity recognition tool that can process over 1000 abstracts per second using a dictionary-based approach. It achieves 70-80% recall and 80-90% precision using comprehensive dictionaries, expansion rules, and a curated blacklist to identify entity types like genes, proteins, chemicals, and diseases. The tool has a C++ engine, is inherently thread-safe, and includes interactive annotation, Python wrappers, and a REST API.

1. Lars Juhl Jensen
Tagger
Rapid dictionary-based named entity
recognition

2. C++ engine

3. flexible matching

4. >1000 abstracts / second

5. inherently thread-safe

6. comprehensive dictionary

7. 70–80% recall

8. expansion rules

9. 80–90% precision

10. curated blacklist

11. entity types & use cases

12. genes/proteins

13. assess studiedness

14. Cannon et al., Bioinformatics, 2017newdrugtargets.org

15. construct networks

16. string-db.org Szklarczyk et al., Nucleic Acids Research, 2017

17. chemicals

18. cellular components

19. tissues

20. diseases

21. organisms

22. environments

23. interactive annotation

24. Pafilis et al., Proceedings of BioCreative V, 2015extract.hcmr.gr

25. real-time tagger

26. BeCalm API

27. Python wrapper

28. 163 lines of code

29. performance

30. tagger.jensenlab.org/BeCalm

32. bottlenecks

33. network latency

34. text retrieval

35. Acknowledgments
Evangelos Pafilis
Sune Pletscher-
Frankild
Damian Szklarczyk
Michael Kuhn