What is NER

NER (named entity recognition) is a Natural Language Processing (NLP) task that finds “entities” in text and labels them with a type. Entities are real-world things people refer to—names, organizations, locations, dates, product identifiers, amounts, emails, or custom domain concepts like “policy number” or “SKU.” Because it turns unstructured text into structured spans, NER is also called “entity extraction” or “entity tagging.”

At a practical level, NER answers two questions at the same time: “Which words belong together as a single entity?” and “What kind of entity is it?”

For example, in the sentence “Ship the order to Spring Garden Road by 17 Jan,” a model might label “Spring Garden Road” as an address/location and “17 Jan” as a date. The output is typically a set of text spans with start/end positions and a label per span.

To build NER, you need a label schema (the entity types you care about) and consistent annotation rules. Training data usually looks like raw text plus entity spans marked in place.

What counts as an entity (and how strict you are about boundaries) depends on the business goal. A customer support workflow might only need product names and order IDs. A contract analytics workflow might need parties, effective dates, governing law, and termination clauses—each with its own edge cases.

A short, practical checklist for getting NER right:

1. Define the label set in business language (what decisions will this power?).
2. Write boundary rules (e.g., do you include “Inc.” in an organization name?).
3. Include “hard negatives” (text that looks like an entity but shouldn’t be labeled).
4. Track disagreements between labelers and tighten guidelines as you learn.

Modern NER systems are typically trained with machine learning, often transformer-based models, but the core challenges are rarely “model problems.” The hard parts are ambiguity and domain shift. Ambiguity shows up when the same string can mean different things (“Apple” the company vs the fruit) or when a token can belong to multiple categories depending on context.

Domain shift shows up when you train on one style of text (like clean news articles) and then deploy on another (or short, informal notes) where abbreviations and spelling errors are common.

Evaluation is usually done with precision, recall, and F1 score at the entity level. That means a partial match is often counted as wrong: if your label is “San Francisco” but the model predicts “Francisco,” it fails that entity. In operations, many teams also measure field-level correctness (e.g., “did we capture the full order ID?”) because that’s what affects downstream automation.

Example:

In e-commerce search, teams may use NER to detect brands, categories, and attribute values inside user queries—“nike running shoes size 10”—so retrieval and ranking behave more predictably.

In that setting, NER interacts closely with search query annotation and taxonomy design: you’re not just labeling words, you’re codifying what your business considers a “brand” or a “size,” and how to normalize variants.

NER is often paired with OCR when the text originates from documents. OCR converts pixels to text, and NER (or other extraction models) turns that text into typed fields. If you need high-quality labeled datasets for entity extraction across messy real-world text—tickets, chats, listings, or extracted document text—Taskmonk’s text annotation workflows help teams keep schemas, QA, and reviewer feedback loops consistent at scale.

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