Entity Extraction

Very robust deep technological stack combining unsupervised (unsupervised morphology (more) and unsupervised part-of-speech tagging (more)) and supervised methods

  • Diseases
  • Chemicals/drugs
  • Genes (and their mutations)
  • Places

This is a place recognition system that is geared towards cities but can also include states, locations etc. It works independent of lists, i.e. it can detect locations that were not present in training data.

  • Persons

Recognize names of natural persons in texts. This is not done via extensive lists but by deep linguistic analysis of the surrounding text. This means that also new names can be identified with a high precision. System can also extract longer names like “John F. Kennedy”.

  • Organizations

Same as Persons but for Companies. Here, the problem is that many normal words (“Apple”) and phrases can be names. Since our system uses the linguistic context, such problems are greatly reduced.

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