Detailing Applications of Named Entity Recognition
10 Sep

Detailing Applications of Named Entity Recognition

In its ‘raw’ state, written language contains an inherent value, but extracting that value from the text (i.e., someone reading it, understanding it, and acting on it) is time and resource-intensive. Recently, organizations are leveraging technologies like named entity recognition (NER) to do just that.

A recent whitepaper from Dovel dives into the practice of NER, the task of identifying and categorizing key people, places, and things in a text. An entity can be any word or series of words that consistently refers to the same thing, such as:

  • Person            Eg., Elvis Presley, Audrey Hepburn, The Pope
  • Organization Eg., Google, Mastercard, University of Oxford
  • Time                Eg., 2006, 16:34, 2am, 100 BC
  • Location         Eg., Trafalgar Square, MoMA, Machu Picchu
  • Work of art    Eg., Hamlet, Guernica, Exile on Main St.

NER technologies first detect a named entity – detecting a word or string of words that form an entity, then categorize the entity, and finally extract the entity in a downloadable format.

The paper details how NER could be applied to medical, legal, and other programs, including

  • Human resources – Speed up the hiring process by summarizing applicants’ resumes; improve internal workflows by correctly categorizing, analyzing, and routing employee complaints and questions quickly and efficiently.
  • Customer support – Improve response times by categorizing user requests, complaints, and questions and filtering by priority keywords.
  • Search and recommendation engines – Improve the speed and relevance of search results and recommendations by summarizing descriptive text, reviews, and discussions.
  • Biomedical organizations – NER is used extensively with biomedical data for gene identification, DNA identification, and identifying drug and disease names in text.

The whitepaper explores how NER can be used to analyze and extract value from raw text data detailing how Dovel uses the technology in its platform. The paper delves into specific models and solutions and looks at specific use cases for the medical, legal, and other fields with illustrated outputs. These examples show how using NER organizations can drive significant insights that can make genuinely profound and positive impacts on their business.