Understanding Named Entity Recognition (NER) in NLP
With AI advancing rapidly, Natural Language Processing (NLP) is undoubtedly a crucial area in how machines comprehend and process natural human language. One major undertaking within this area of NLP is Named Entity Recognition (NER) – the ability for machines to identify and classify information in the text automatically.
NER supports the identification of information such as names of people, organizations, products, and locations, from unstructured documents and the expression of that information in structured data. This is especially useful for search engines, chatbots, healthcare systems, legal document review, news aggregation, etc. You can think of Named Entity Recognition as a step toward bridging the gap between unstructured text and actionable data, so that machines can communicate with people in a more sophisticated manner.
This blog will take a deep dive into how Named Entity Recognition works, the types of models (rule-based versus transformer-based BERT) that have been used to implement NER, and use cases for Named Entity Recognition across different industries. By the time you read this article, you will undoubtedly understand the deep significance that NER has played in the context of the modern AI framework and the intelligent systems we use daily.
New to NLP? Start with our beginner-friendly Natural Language Processing Guide.
What is Named Entity Recognition (NER)?
Named Entity Recognition (NER) is a type of information extraction, which means a machine extracts named entities in the text and then classifies them into a predefined category. The categories are typically something like this:
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| Entity Extraction |
- Names of people (for example: Mark Zuckerberg)
- Organizations (for example: Google, Meta)
- Locations (for example: Paris, United States)
- Dates and times (for example: January 1, 2025)
- Quantities (for example: 10 KG, 100 USD)
- Other (for example: product names, events, etc.)
Example Sentence:
Microsoft was founded by Bill Gates in Albuquerque, New Mexico, in 1975."
NER would extract:
- Organization: Microsoft
- Person: Bill Gates
- Location: Albuquerque, New Mexico
- Date: 1975
Why is NER important in NLP?
NER is important in transforming unstructured text into structured data, enabling:
- Integrated Search (E.g., location(s) on travel blogs)
- More intelligent / contextually aware chatbots
- Gathering information from legal, financial, and medical texts
- Augment sentiment analysis by knowing which opinion is tied to which entity
NER is also found in voice assistants, resume parsers, cyber threats, and monitoring social media.
How NET works: Rule-based VS Machine Learning based Approaches
- Rule-based NER
Rule-based NET depends on:
- Predefined pattern
- Lexicons and dictionaries
- Regular expression
The advantage of Rule-based NER is that it is reasonably simple and interpretable, while on the other hand, it has some disadvantages, such as being Brittle and limited in scalability.
- Statistical and ML-Based NER
Statistical and ML-based NER implement supervised learning methods using an annotated corpus:
- Conditional Random Fields (CRF)
- Hidden Markov Models (HMM)
- Support Vector Machines (SVM)
It is more flexible and adaptable, while it requires larger labelled datasets.
- Deep Learning-Based NER
Deep learning-based NER implements models like:
- BiLSTM-CRF stands for Bidirectional LSTM and CRF
- Transformers that include BERT and RoBERTa
Deep learning-based NER can exploit context and sequence dependency to achieve higher accuracy.
NER Datasets and Tools
Common Datasets:
The following are some of the popular and common datasets used in NER
- CoNLL-2003: English newswire dataset
- OntoNotes 5.0: Includes multiple domains and languages
- WNUT 2017: Focuses on emerging and informal entities (social media)
NER Tools and Libraries:
Some of the common and popular NER tools and Libraries are as follows:
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| Popular Tools and Libraries for Named Entity Recognition (NER) in NLP |
The Role of Annotated Datasets in NER Evolution
The field of named entity recognition has been strongly influenced by the availability and quality of annotated datasets throughout its development. Early manually labelled corpora, such as the Message Understanding Conference (MUC) datasets, were essential in developing rule-based and statistical models. As NLP matured, we saw datasets such as CoNLL-2003 and OntoNotes 5.0 begin adding additional metadata and finer-grained annotations so that when we built systems, they could better recognize contextual and domain-specific language. Now, with crowdsourced labelled datasets, active learning techniques, and transfer learning, we are rapidly designing high-quality NER systems in low-resource languages as well. The richness of these data sources is important for increasing accuracy in entity recognition and generalization across domains; thus, they are as critical as the models they are designed to be used with.
Problems with NER
NET systems face several obstacles:
- Ambiguity: “Apple” as either a company or a fruit?
- Nested Entities: “President of the United States Barack Obama”
- Domain Adaptation: While news NER may work fine, medical or legal NER may not work at all.
- Low-Resource Languages: Training data on non-English is very limited.
- Multilingual Contexts: Some data sources, like Urdu-English tweets, can be multilingual.
Applications of Named Entity Recognition
Applications of Named Entity Recognition (NER) are commonly applied to practical real-world use cases:
- Search Engine Optimization (SEO)
NER is used in SEO to recognize entities in user queries, increasing the relevancy of a search.
- Customer Support Automation
NER enables Bots that identify people, products, and actions to assist them navigate faster.
- Financial Sector
NER in the financial sector is used to extract the names of companies, currencies, and events from financial reports or financial news articles.
- Health Sector
NER in the health sector is used to identify symptoms, treatments, and drugs in patients’ records or medical research papers.
- Legal Text Extraction
NER automates the extraction of names, law, and case references.
Comparing NER to Other NLP Tasks
NER often works in parallel with:
- Text Classification (to categorize documents)
- Sentiment Analysis (to assign sentiment to entities)
- Coreference Resolution (to refer pronouns back to entities)
For example, when given these sentences:
Barack Obama gave a speech. He emphasized…
NER identifies “Barack Obama”, and coreference links “He” to “Obama”
Curious how text is initially categorized before NER? Read our complete guide on Text Classification in NLP.
Closing Remarks: The Potential of Named Entity Recognition in NLP
Specifically, Named Entity Recognition (NER) has built on the standard rule-based methods and used contemporary machine learning and deep learning frameworks. The work NER does - from parsing real-time social media feeds for meaningful insights, to improving search engine accuracy, implementing AI assistants - is a crucial step toward allowing machines to exhibit greater language awareness.
As the field of NLP continues to advance, we should expect NER systems to see increases in accuracy, improve multilingual support, and exhibit better contextual awareness. The new models like BERT and GPT have greatly improved our understanding of how to recognize nuanced entities across domains and languages, and as future advances such as few-shot learning and real-time NER start to appear, NER will become increasingly adaptive and thorough.
If you or your business is building or learning about AI-powered applications, you should understand NER to build a strong understanding of how to structure data, utilize automation, and provide intelligent user experiences.
Interested in going further into NLP? Be sure to check out our previous blogs on Natural Language Processing, Text Classification, and Sentiment Analysis, so you can build your complete understanding.
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