Recruiter's Glossary

Examples: Semantic Networks • A* Algorithm • Markov Chains

Named Entity Recognition

Term from Artificial Intelligence industry explained for recruiters

Named Entity Recognition (NER) is a technology that helps computers automatically find and label important information in text, like names of people, companies, locations, dates, and other key details. Think of it like a smart highlighter that can automatically go through documents and mark different types of information in different colors. For example, it can identify "Apple" as a company name in one sentence and as a fruit in another. This technology is widely used in various business applications like processing customer service emails, analyzing legal documents, or organizing large amounts of text data. You might also see it referred to as "entity extraction" or "entity identification."

Examples in Resumes

Developed Named Entity Recognition models to automatically extract customer information from support tickets

Improved accuracy of NER systems for processing legal documents by 40%

Led team implementing Named Entity Recognition and Entity Extraction solutions for healthcare records

Typical job title: "NLP Engineers"

Also try searching for:

NLP Engineer Machine Learning Engineer AI Engineer Natural Language Processing Engineer Data Scientist AI Developer Text Analytics Engineer

Where to Find NLP Engineers

Online Communities

Professional Networks

Conferences & Events

Example Interview Questions

Senior Level Questions

Q: How would you approach building a Named Entity Recognition system for a new industry domain?

Expected Answer: A strong answer should discuss analyzing domain-specific documents, planning data collection and labeling, choosing between existing models or building custom ones, and establishing ways to measure accuracy. They should mention the importance of working with domain experts and handling industry-specific terminology.

Q: How do you handle ambiguous entities in text?

Expected Answer: Should explain how context helps distinguish between different meanings (like Apple the company vs. apple the fruit), and discuss techniques for improving accuracy when words have multiple possible meanings.

Mid Level Questions

Q: What approaches would you use to improve NER accuracy when you have limited labeled data?

Expected Answer: Should discuss practical solutions like using pre-trained models, data augmentation techniques, and active learning approaches where you strategically choose which data to label.

Q: How do you evaluate the performance of a Named Entity Recognition system?

Expected Answer: Should explain common evaluation metrics in simple terms, like how many entities were correctly identified, missed, or incorrectly labeled, and how to interpret these results.

Junior Level Questions

Q: What is Named Entity Recognition and what are some common use cases?

Expected Answer: Should be able to explain in simple terms how NER identifies important information in text, and give examples like finding company names in news articles or extracting dates from emails.

Q: What are the common types of entities that NER systems typically identify?

Expected Answer: Should list and explain common entity types like person names, organizations, locations, dates, and describe how these are useful in real applications.