With three annotators, we used brat (brat.nlplab.org/) to comment on an example of texts for three categories: PERS, ORG, GPE. I would like to calculate the inter-annotator agreement for this example. However, I do not seem to find an easy way to do that. I tried this Python: github.com/savkov/BratUtils package, but it seems to fail. I have developed a tool for calculating the agreement of annotators. It mainly caters to Doccano, but with few adjustments, it might be able to calculate everything. Take a look at: github.com/vwoloszyn/diaa/ A collection of utilities for data processing and calculation of Inter-Annotator agreement in brat annotation files. We can imagine a note as a tripel (d, l, o) where d is a document identifier, l a name, and o a list of end character offset tuples. An annotator i contributes to a (multi-) set ai of annotations (tokens). We calculate F1ij = 2 | Ai ∩ Aj | / (| Ai| + | Aj|) for each combination of 2 annotators and ratio of arithmetic mean and standard deviation of F1 over all these combinations (see Hripcsak & Rothschild, 2005).
Grouping comments by document or label allows us to calculate F1 per document or label. Is there a simple way to calculate Inter Annotator Agreement (with Python or a web-based tool)? You can request the full text of this conference paper directly from the authors on ResearchGate. This library basically follows the definitions of accuracy and recall calculation from the MUC-7 test score. The basic definitions as well as some additional restrictions are listed below: To read the full text of this research, you can request a copy directly from the authors. . Disclaimer: The current definition of the PARTIAL category offers room to work with syntactic chunks. Another layout (for example, select the largest included day as a partial match instead of the far right) might be more appropriate for other tasks, z.B. for certain types of semantic comments. Install as a normal package from the source directory. If nothing happens, download GitHub Desktop and try again. .
Note: The gold standard is considered to be the collections/documents from which the comparison is called, while the parallel score indicated is considered a set of candidates. For each example commented, I have three .ann files for which I want to calculate Inter Annotator Agreement. The data in the files are as follows: Kolditz, T., Lohr, C., Hellrich, J., Modersohn, L., Betz, B., Kiehntopf, M., &Hahn, U. (2019). Note of German clinical documents for de-identification. In MedInfo 2019 – Proceedings of the 17th World Congress on Medical and Health Informatics. Lyon, Frankreich, 25.-30. August 2019. IOS Press, S.
203-207. . Die Übereinstimmung in Multi-Token-Anmerkungen wird üblicherweise mit f-score bewertet. . . .