Named Entity Recognition (NER): Understanding Entity Extraction in NLP

December 14, 2023

Named Entity Recognition (NER), also called entity chunking or entity extraction, is a Natural Language Processing (NLP) technique that involves identifying and categorizing key information, entities, in texts. An entity can be any word or series of words that refer to the same theme, and each detected entity is classified into a predetermined category. For example, entities can be names of people, organizations, locations, times, quantities, monetary values, percentages, and others.

Main use cases

Extracting important information from texts is crucial for various activities. Therefore, the applications of NER are broad and applicable to various sectors. Some examples include:

1. Optimization of SEO algorithms (Search Engine Optimization).

2. Development of algorithms for recommendation systems that provide suggestions based on search history or current activity.

3. Automatic translation systems.

4. Categorization of articles and news into predefined categories such as sports, politics, current affairs, and more.

5. Customer support, improving the understanding and accuracy of chatbots.

6. Resume filtering systems.

NER example

ner example

In the sentence above, it is possible to identify various types of entities, such as:

"Jeff Bezos" is labeled as PERSON, indicating that it is an entity representing a person's name.
"Amazon" is tagged as ORG, meaning Organization. This means it is recognized as an entity that refers to companies, institutions, etc.
“1994” is marked as DATE, representing a period.
"Seattle" has been classified as LOC, which stands for location entity. It represents countries, cities, states, or any other regions with a defined boundary or governance.

In essence, NER is the technique to use to identify who, where, what, and when of a text.

NER Application

It is recommended to use semi-supervised techniques for NER algorithms to reduce manual efforts in training, as they typically require a large amount of manually labeled data.

During training, the goal is not for the algorithm to "memorize" examples, but to identify and classify keywords within a similar context. For instance, when training an algorithm with texts containing "Elint Tech," the aim is not for the algorithm to learn that "Elint Tech" is a company but that in similar contexts, this name should be classified as a company.

To train a model effectively, specific techniques are used. Simply showing the model an example text once is not enough, especially if there are limited examples available. Therefore, the text is iterated over a certain number of times, with each iteration shuffling the text to prevent the model from generalizing based on the order of examples.

Another practice in training NER algorithms is the use of dropout rates. Thus, in each iteration, a percentage of examples will be removed, further reducing the chance of memorizing training examples.

A widely used library for training and classifying texts in Python is spaCy. It provides a statistically efficient system for NER, simplifying the application of NLP in this language. Code examples can be found at this link. Additionally, spaCy now integrates seamlessly with Large Language Models (LLMs) through the spacy-llm package. This integration enhances spaCy pipelines, enabling fast prototyping and robust outputs for various NLP tasks without the need for additional training data.

In this way, we can use one of the world's most popular programming languages to quickly create NLP models that recognize a range of words, such as company names, organizations, locations, product names, and news categories, among many other applications.

Curious to explore the potential of Named Entity Recognition for your business applications? Consider Elint Tech, your dedicated partner in custom software development. Discover more about our NLP capabilities here.