top of page

Diving into the World of NER

What exactly is NER, you might ask? NER, which stands for Named Entity Recognition, is like a fun game where our computer program hunts for specific types of information in a massive sea of words! It's part of a bigger field known as Natural Language Processing (NLP) – think of it like teaching machines to understand human language.

Imagine you're reading a novel and circling every character's name, city, date, or time you come across – that's pretty much what NER does. Named entities, in technical terms, are specific pieces of data like names of people, companies, places, dates, times, and even numerical quantities.

Why is NER cool?


NER is kind of like a super-sleuth for data extraction. It uncovers key nuggets of information hidden within loads of text. This is super useful for search engines (like Google), for answering questions (like Siri), and for translating text from one language to another (like Google Translate).

What kind of named entities are there?


There's a wide variety of named entities, but here are some of the usual suspects:

  • Person: Names like John Smith, Mary Jones, etc.

  • Organization: Company names like Google, Microsoft, Apple, etc.

  • Location: Places like New York City, London, Paris, etc.

  • Date: Specific dates like January 1, 2023, or times like 10:00 AM.

  • Time: Moments in the day, like 10:00 AM, 3:00 PM, etc.

  • Quantity: Numerical values like 100, 1000, 1 million, etc.


What kind of tasks does NER perform?


NER essentially has two main jobs:

  • Named Entity Identification (IE): Think of this as the game where you circle or highlight named entities in a paragraph or article.

  • Named Entity Classification (CE): This is where you'd label or categorize the highlighted named entities based on their types, like person, organization, or location.


Where do we use NER?


NER has a lot of cool applications! Here are a few:

  • Search engines: NER helps pull out important pieces of data from heaps of text, like the names of people, companies, and places. This can help in giving better, more accurate search results.

  • Question answering systems: NER helps extract necessary info from texts to answer user questions. This is what makes smart assistants like Alexa or Siri so clever!

  • Machine translation: When translating text from one language to another, NER can identify named entities, helping to increase the translation's accuracy. That's why your foreign language homework looks so perfect when translated!

Digging Deeper into NER with Features

Unearthing Features Just like how different characteristics help us recognize our friends (like hair color, height, etc.), 'features' in text help us identify and classify named entities. Features are a little like clues that our NER detective uses to identify entities.

Common features are:

  • The actual words making up the named entity

  • The capitalization of these words

  • The entity's location in the sentence

  • The sentence context surrounding the entity

Selecting the Right Features However, not all features are equally helpful. This is where 'feature selection' steps in. It's like choosing the right tools for a job, selecting only those features that are most important for our NER tasks.

Classifying with NER With the right features in hand, it's time to sort or classify these named entities. To do this, we can use various classification methods, like:

  • Support Vector Machines (SVMs): Think of these as a clever method that finds the best boundary line that separates different types of entities.

  • Naive Bayes classifiers: These use the power of probability to classify named entities.

  • Decision trees: These make a tree of choices to decide the category of an entity.


NER with a Statistical Twist

Hidden Markov Models HMMs are like magical mathematical models that can capture patterns in sequences of data. They're handy for NER because they can model sequences of words in named entities.

Getting Conditional with Conditional Random Fields (CRFs) CRFs, another type of statistical model, take it a step further. They can capture patterns and also understand dependencies in sequences of data. They can understand how words in a named entity relate to each other.

Grading NER Performance Just like we have exams at school, we also need to evaluate how well our NER system is doing. Some common ways to measure this are:

  • Precision: This tells us how accurate our system is in identifying named entities correctly.

  • Recall: This measures how well our system is in not just identifying, but also correctly classifying named entities.

  • F1 score: This is a fancy score that combines both precision and recall to give an overall performance rating.


  • Build an NER system using the magic of HMMs.

  • Construct an NER system harnessing the power of CRFs.

  • Measure how well your NER systems perform.


NER Takes a Deep Learning Dive

Looping with Recurrent Neural Networks (RNNs) R


NNs are special types of neural networks that are really good at understanding sequences of data. They're ideal for NER as they can make sense of sequences of words in named entities.

Scanning with Convolutional Neural Networks (CNNs)


CNNs are another kind of neural network that can find features from sequences of data. They can help in NER by extracting features from sequences of words within named entities.

Unleashing Deep Learning for NER


Deep learning has proven to be a game changer for NER. It's like a high-tech upgrade! There are several powerful models that can be used for NER, including:

  • Bidirectional LSTMs: These are RNNs that can remember both past and future data, making them very powerful.

  • CNNs: As we mentioned earlier, these are excellent for feature extraction.

  • Transformer models: These are the latest in NER tech, making sense of word context like no other model.



  • Construct an NER system using the super smart Recurrent Neural Networks (RNNs).

  • Build an NER system that leverages the feature-extracting prowess of Convolutional Neural Networks (CNNs).

  • Measure the performance of your freshly minted NER systems.

Advanced Techniques for NER

Embracing Embeddings When dealing with text, 'embeddings' are a super cool way to represent words as vectors (fancy term for lists of numbers). These vectors help capture the meaning and context of a word, which is extremely useful for our NER tasks.

Harnessing the Power of Attention 'Attention' is a groundbreaking technique in deep learning. It's like when you're in a noisy room but can still focus on what your best friend is saying. Similarly, 'attention' in NER helps the model focus on important words when processing text.

Transforming NER with Transformers


The 'Transformer' model is like the star quarterback of NER. It uses attention mechanisms to understand the context of words better than any model before. It's transforming (pun intended!) the way we do NER.

Your Assignment

  • Implement word embeddings in an NER system.

  • Use the attention mechanism in an NER system.

  • Explore using Transformer models for NER.

  • Evaluate the performance of your advanced NER systems.

Future Directions in NER

The fascinating world of NER is continuously evolving. New techniques and models are being developed that push the boundaries of what's possible. Some of the exciting areas to keep an eye on include:

  • Transfer learning: This is like applying what you learned in one subject to another. In NER, it means using knowledge gained from one NLP task to improve performance on another.

  • Multilingual NER: This involves training NER systems that can recognize named entities across multiple languages. It's like having a multilingual super-sleuth at your disposal!

  • Cross-domain NER: This is about developing NER systems that can identify and classify named entities across different topic areas or domains.

bottom of page