NER, or Named Entity Recognition, is a deep learning text analytics subdomain where terms and phrases are identified within unstructured text and classified into categorical (aka entity) types (e.g. proteins, cell line, disease, companies, etc.). We use NER tagging to create NER Concepts, which appear in several areas of Layar. Some examples of how we leverage NER Concepts include Filters By Concept Type, the Document View, and the NER Concept Graph.

Here are some example NER tags that we use to annotate texts:

Life Science NER Tags

We have built out over twenty different NER concept types for the life science domain, ranging from diseases to proteins to simple chemicals. This list continues to grow every day - but here is an example of some of the NER concepts we can pick up:

Anatomical System
Cancer
Cell
Cell Line
Cell Type
Cellular Component
Chemical
Chemical of Biological Interest
Disease
Gene Ontology
Gene or Gene Product
Immaterial Anatomical Entity
Location
Multi Tissue Structure
Organ
Organism Subdivision
Protein
Sequence Ontology
Simple Chemical
Tissue

Business Development NER Tags

Organization - a company, non-profit, or institution. 
Person - an author, board member, etc.
Location - a country, city, region, etc.


Custom NER Tags

We can also provide custom NER tagging models for your team. Please contact us for more details.

Did this answer your question?