NER is a part of the information extraction process. First, the annotator locates and classifies named entities present in the unstructured text. Then, these named entities are put into predefined categories.
The categories may include organizations, person names, locations, time expressions, medical codes, percentages, quantities, monetary values, etc. It is one of the vital entity detection methods when it comes to natural language processing.
By using NER, you can automatically scan entire documents and extract fundamental entities from them.
Using UBIAI’s auto-labeling feature can be helpful in the following ways:
For auto-labeling and annotation of words with UBIAI, you can associate one or more dictionaries. The dictionary will help in entity labeling and, ultimately, pre-annotation of data.
The dictionary file should contain every associated word with the corresponding entity type.
Rule-based matching allows auto-labeling of documents by combining multiple rules.
It will enable instant auto-labeling of documents by using multiple pre-defined rules such as regular expressions, Part of Speech (POS), and patterns (email, number, phone number, etc.…).
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