What Is Named Entity Recognition (NER)? Key Concepts and Approaches

What is Named Entity Recognition (NER)?

The Named Entity Recognition (NER), or named entity recognition, is a technology derived from automatic natural language processing (NLP).

It makes it possible to automatically identify, in a plain text, key elements such as:

• names of people,
• geographical locations,
• dates,
• financial amounts, percentages, quantities, etc.,
• Organizations
• or even units of measurement.

Its objective is simple: to transform unstructured content into data that can be used by a machine.

Concretely, the NER is based on two main stages:

• The detection of named entities : identify specific words or expressions in a text.
• The classification : assign to each detected entity a predefined category (person, location, organization, etc.).

Historically, the first NER systems were based on simple rules or Fuzzy Matching, consisting in comparing character strings with reference lists while allowing small differences (accents, typos, abbreviations...).

These approaches, while effective in some cases, lacked robustness and precision in varied or noisy contexts. They have since been greatly enriched by more advanced methods, in particular by deep learning and semantic embeddings.

When properly deployed, NER brings concrete benefits in many business contexts:

• Automates the extraction of relevant information from large volumes of text
• Transform unstructured data into actionable and organized information
• Facilitates identifying emerging trends and following weak signals
• Reduces human errors in analysis and reading processes
• Accelerates processing in all business sectors, from legal to finance
• Frees teams from repetitive tasks with low added value
• Improves the accuracy and efficiency of natural language processing (NLP) tools

How does NER work?

This Named Entity Recognition process follows a series of structured steps, combining linguistic, statistical, and machine learning techniques.

Here are the main phases in the functioning of the NER:

1. Tokenization

It all starts with the Tokenization, which consists of dividing plain text into elementary units called Tokens : words, punctuation marks, dates, numbers... This segmentation makes it possible to prepare the ground for the next steps of linguistic analysis.

For example, the sentence:

will be segmented into:

["The", "48th", "World", "Hospital", "Congress", "will", "take", "place", "in", "Geneva", "from", "November", "10", "to", "13", ",", "2025", "."]

2. Entity Identification

The second step is to identify word groups that could correspond to named entities. This detection is based on:

• Of linguistic characteristics such as capitalization, position in the sentence, or punctuation;
• Of contextual clues (for example, a date is often preceded by a preposition such as “en” or “in”);
• Of lexical resources such as lists of names of cities or businesses (gazetteers).

The objective here is to identify segments in the text flow that “look” like entities.

3. Entity Classification

Once potential entities are detected, the system classify into predefined categories.

This ranking is generally carried out by a trained model on annotated datasets. Algorithms such as CRFs (Conditional Random Fields) or neural networks are commonly used for this task.

Understanding these categories is critical to fully exploiting NER's capabilities. Here is an overview of the most common types:

4. Contextual Analysis

The context is essential to ensure the accuracy of the NER. Some words or names may refer to different entities depending on usage.

Contextual analysis makes it possible to remove these ambiguities by taking into account neighboring words, syntax, and even the structure of the document. It also allows you to manage nested entities (for example: “President Barack Obama of the United States” contains two distinct entities).

With modern models, expanding the analysis context significantly improves the disambiguation. It is now possible to use a prompt to automatically compare a detected entity to a list of several thousand items (e.g. 100,000 business names), by short-listing the closest matches.

5. Post-processing

Finally, a post-treatment phase refines the results:

• merging multi-word entities (e.g. “San Francisco” as a single entity),
• management of duplicates or overlaps,
• validation with external databases or business rules.

This step can also generate structured output, such as a JSON or XML file, where each entity is tagged, making it easy to integrate into an information system or an automated process.

The main approaches of the NER

Several approaches have been developed to effectively implement named entity recognition (NER). Here is a detailed explanation of the most common methods:

1. Rule-based approaches

Rule-based NER systems operate on the basis of manually defined linguistic models. In particular, it includes:

• Regular expressions, which make it possible to detect entities according to specific forms (such as telephone numbers, email addresses or dates).
• Dictionaries or lexicons (often called Gazetteers), which compare the words in the text to pre-existing lists of proper names, places, businesses, etc.
• Syntactic or contextual rules, which identify entities based on their position or function in the sentence (e.g., a capitalized word preceded by “M.” can refer to a person).

2. Machine Learning Approaches

Machine learning-based methods involve training a statistical model on annotated examples so that it can learn to recognize entities.

• The model analyzes various characteristics of the text (such as capitalization, suffixes, grammatical labels, or immediate context).
• Among the commonly used algorithms, we find CRF (Conditional Random Fields), the SVM (Support Vector Machines) or decision trees.

3. Deep Learning Approaches

Deep learning has dramatically improved NER performance, relying on neural networks that can learn directly from plain text.

• Les recurring networks (RNN, LSTM) allow you to take into account word order and long-term dependencies in the sentence.
• Les Transformers models, like BERT, analyze the text as a whole and take into account the full context to better disambiguate entities (e.g., “Apple” as a company or a fruit).

4. Hybrid approaches

Hybrid systems combine many of the above methods to take advantage of their respective advantages. For example:

• Preprocessing based on rules or dictionaries can make it possible to quickly identify simple entities before applying a more advanced machine learning model.
• A BERT model can be enriched with business rules or custom lists to improve accuracy in a specific sector.

Moreover, some recent hybrid approaches combine semantic embeddings and fuzzy matching to calculate the similarity between a detected entity and external databases. This makes it possible to intelligently identify matches even if the character strings differ.

The main applications of NER in business

Best practices for deploying NER effectively

To ensure good performance and optimal accuracy, the implementation of NER must follow several key steps. Here are the key recommendations:

Tools and libraries for named entity recognition (NER)

Depending on your goals — rapid integration, advanced customization, or large-scale processing — you can opt for open source libraries Or ready-to-use cloud services.

The most used open source libraries

These solutions are particularly suited to custom projects and Python or Java development environments. Here are the three most popular:

SpacY‍

Renowned for its speed and ease of integration, SpacY is now one of the most used NLP libraries in production. It offers pre-trained models for NER in several languages and allows effective fine-tuning. Its ecosystem is well documented and largely maintained by the community.

Flair‍

Developed by Zalando Research, Flair makes it possible to combine several deep learning models (such as BERT, ElMo) to improve the accuracy of extracted entities. It is distinguished by its multilingual support and its flexibility in research or experimental projects.

Stanford CoreNLP‍

Robust tool, particularly appreciated for its linguistic precision and its multilingual support. Developed in Java with Python wrappers available, CoreNLP remains an academic and professional reference, although more demanding in terms of system resources.

Cloud services (turnkey NER APIs)

Ideal for businesses that want to quickly integrate NER into their systems, without managing model training or hosting.

Google Cloud Natural Language API‍

Offers rich entity extraction, with categorization, relevance score, and syntactic analysis. Perfect for large-scale cloud applications.‍

Amazon Comprehend‍

Native NER solution in the AWS ecosystem. It automatically identifies entities (names, places, dates...) and is easily integrated into serverless architectures or automated processing pipelines.‍

IBM Watson Natural Language Understanding‍

A comprehensive API for large accounts, which goes beyond NER. It also makes it possible to analyze emotions, semantic relationships, concepts or intentions, with advanced levels of configuration.

Obstacles to a reliable and accurate NER

Despite its promising performances, NER is still facing several limitations that are important to anticipate in order to ensure effective implementation.

Ambiguity of terms

The same word can refer to several types of entities depending on the domain or current usage.
For example:

• “Amazon” can refer to a venture (e-commerce) or a River.
• “Orange” Maybe a color, a fruition, or a Telecommunication brand.

Without disambiguation, models may mislabel these entities, especially in short or ambiguous contexts.

Context dependence

The meaning of an entity also depends on Its position in the sentence and syntactic relationships.

Let's take the following example:

Here, “Renault” is indeed a organizing.
‍

But in:

The same word is associated with sports stable, and not to the car company in its strict sense.

Modern models like BERT or Roberta, trained on bi-directional contexts, are able to capture these nuances to improve classification.

Multilingual complexity

Languages have differences in syntax, capitalization, or entity formats. Some languages don't have clear conventions for proper nouns. The NER must adapt to these variations, often using multilingual or language-by-language trained models.

Limited annotated data

Supervised learning requires annotated corpora, which are often unavailable in certain sectors (legal, medical, etc.) or for languages that are poorly represented. This lack of data limits the performance of the models.

Bias and lack of robustness

NER models can integrate biases present in training data (gender, origin, sector...). They are also sensitive to typos, oral errors or to infrequent formulations, which weakens their use in production.

The combined use of semantic embeddings and fuzzy matching considerably improves robustness, by making it possible to detect matches between close strings

In addition, modern techniques for shortlisting entities via similarity scoring, then validation by prompt, provide greater reliability than traditional machine learning models, especially in rich and ambiguous business environments.

From entity recognition to intelligent data extraction

The Named Entity Recognition is now part of documentary solutions much more advanced.

This is particularly the case for OCR Smart, which are part of the wider field ofIntelligent Document Processing (IDP).
‍
Far beyond simply reading text, these tools use advanced technologies such as computer vision, natural language processing (NLP) or even named entity recognition (NER) to automatically extract structured information with high added value.

They allow various documents to be analyzed accurately, such as:

• invoices (amounts, suppliers, item lines),
• contracts (clauses, dates, signatories),
• Pay slips (net salary/gross, contributions),
• proof of address,
• bank statements, etc.

Solutions like Koncile are based on a combination of complementary technologies to offer reliable, contextualized and immediately usable extraction:

• High precision OCR, capable of reading complex documents (invoices, pay slips, contracts, etc.) reliably, regardless of layout or format variations;
• Extracting key business fields, using a combination of computer vision and LLM, to accurately identify information such as supplier name, SIRET number, HT/TTC/VAT amounts, dates and references;
• Detailed line-by-line recognition, making it possible to return invoice tables (designation, quantities, unit prices, discounts, etc.) with a high level of structuring, even in case of complexity;
• Advanced customization, with configuration of the fields to be extracted, dynamic adaptation to various documents and compatibility with natural language queries;
• Standardized output formats (JSON, Excel, API), which can be directly integrated into existing accounting or ERP systems.

By combining linguistic vision, statistics and understanding the context, NER is at the heart of automated document processing chains.