Source: This use-case was kindly contributed by Maria Biryukov, University of Luxembourg, Center for Contemporary and Digital History.
We used INCEpTION to annotate multi-lingual company inventory data with various entity types. Besides widely used classes, such as PERSON, LOCATION, ORGANIZATION, we introduced additional entity types, which would enable us to capture notions of duration, company structure, and person function or role in the company lifecycle.
The main goal of using INCEpTION at this stage of the project was to prepare a gold standard data for training a recurrent neural network for the task of named entity recognition. Features of INCEpTION that turned to be especially helpful in our scenario are:
- Active learning. This feature considerably reduces time required for the annotation.
- Ability to work with multiple languages. Experience shows, that availability of language and data-specific models has key influence on the named entity recognition results. With INCEPtION, we are able to prepare training data sets tailored for our needs.
- Ability to monitor inter-annotator agreement. This feature helps to produce data sets of higher quality.