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Named Entity Recognition: Mechanism, Methods, Use Cases, and Implementation Tips

NER is popular because it can automatically find and categorize key information from large pieces of text. This allows organizations to easily extract needed data from customer interactions, financial reports, legal contracts, social media, etc.

This guide provides all the information about Named Entity Recognition. You'll learn how NER works, what named entity recognition custom entities are, discover its use cases, and learn how to build named entity recognition model.

Named entity recognition means a technique in natural language processing used to recognize specific types of entities in a text, like names, places, dates, and organizations. Named entity recognition history began in 1996. NER mostly used rule-based methods and ontologies at that time. By 2007, NER models started to adopt machine learning to engineer features. 

Implementation of deep learning has greatly improved NER. Deep learning models for named entity recognition are more flexible and can manage different domains and new data by using characters, sub-words, and word embeddings.

Machines can process large amounts of text and extract key information in organized named entity recognition categories. By identifying specific entities within the text, NER changes how we manage and use written information.

Named entity recognition has several great benefits, but also some limitations. We’ll explain both to you.

Advantages of Using NER

The main advantage of NER is that it helps us find important details in large amounts of textual data, like articles, social media posts, websites, and research papers. Let’s check out some more benefits of NER:

• Enhanced user experience: NER improves customer experience by providing better search results and personalized recommendations.
• Simple analysis of data and trends: NER makes it easier to analyze data to identify tendencies.  
• Automated workflows: NER automates processes, which helps save our time and resources.

Any Limitations of NER?

Now let's look at some named entity recognition challenges:

• Context misunderstanding: algorithms often struggle to understand context because words get meaning from the surrounding text. For example, the word "Bat" can mean a flying animal or a baseball tool (depending on the context).
• Language variation: human language includes slang, dialects, and regional differences, which can make it harder to understand words that are common in one place but not in another.
• Data sparsity: machine learning models need a lot of labeled data to identify entities. This can be difficult to find, especially for rare languages or specialized areas.

Named Entity Recognition NLP uses unique algorithms with grammar rules and statistical models to find and tag names in text. It identifies categories like people, locations, dates, percentages,  organizations, and currency amounts. These categories often use abbreviations, such as LOC for location, PER for person, and ORG for organization.

Once the named entity recognition best model can work with labeled text data, it automatically analyzes new text, identifies named entities, and sorts them into categories. 

After identifying the information, a tool collects details about these entities and creates a machine-readable document. Other tools can then use this document to extract additional information.

To develop the best model for named entity recognition, the text must go through various steps like tokenization and tagging. In tagging, each word in a sentence is labeled with tags like “Person” or “Location”.  We’ll explain what techniques NER uses: 

IO Tagging

In this simple tagging method, each word in a sentence is tagged as “inside” (I) if it's part of an entity or “outside” (O) if it's not.

In the sentence “Sara is going to London,” the words “Sara” and “London” are tagged as entities (I tag), while the words "is," "going," and "to" are not entities (O tag).

This method has limitations, especially with tagging consecutive entities of the same type.

BIO / IOB Tagging

IOB is a widely used tagging method. This system labels each word to show if it is at the beginning (B) of a named entity, inside (I) of a named entity, or outside (O) of any named entity.

In the sentence “Sara is going to London,” “Sara” is marked as “B-PER” to show it's the start of a person's name, while “London” is tagged as “I-LOC” because it's a place.

IOE Tagging

This approach is similar to IOB, but it uses an E tag to mark the end of an entity instead of the beginning.

BILOU Tagging

BILOU is a more detailed version of the BIO labeling system that detects entities. It includes two extra labels, Last (L) and Unit (U), to improve ner metrics accuracy, especially with longer entities.

In a sentence like “Steve Jobs was born in San Francisco,” BILOU tagging would be: Steve —  B-PER (beginning of a Person entity), Jobs —  L-PER (last token of a Person entity), was —  O (outside any named entity), born —  O, in —  O, San Francisco —  U-LOC (Location).

Conditional Random Fields (CRFs)

CRFs are statistical tools that help make structured predictions. They consider the context and relationships between nearby tokens. CRFs are quite practical for custom named entity recognition, which involve labeling sequences.

In the sentence “Samsung announced the new Galaxy Buds in California,” Samsung is marked as an organization (B-ORG), Galaxy Buds as a product (B-PROD), and California as a location (B-LOC). CRFs use the context and relationships between words to identify these distinctions.

Named entity recognition data augmentation uses different methodologies to help identify and classify names and other specific entities in large texts. Let's see what they are.

Named Entity Recognition Methods Based on Rules

Rule-based methods often use linguistic patterns, expressions, or dictionaries. They are effective in tasks like extracting standard medical terms from clinical notes. Yet, rule-based techniques can't handle a large named entity recognition dataset because they follow fixed rules.

Statistical Methods

Statistical methods, like  Conditional Random Fields (CRF) and Hidden Markov Models (HMM), use probabilities learned from training data to indicate named entities. These methods work well with lots of labeled data because they can adapt to different types of text. 

Machine Learning Methods 

Machine learning techniques use algorithms like support vector machines and decision trees to learn from labeled data and predict specific named entities. These techniques help run large datasets and complex patterns. 

Deep Learning Methods

Deep learning methods are the latest development that uses neural networks. Techniques like Recurrent Neural Networks (RNNs) and transformers are popular because they can handle long-term text patterns. These methods work great for big tasks with lots of training data, but they require a lot of computing power.

Hybrid Methodologies

No single method works for every situation in NER. So, this led to the development of hybrid methods. They use a mix of rules, stats, and machine learning to get the best of each. For example, rule-based methods can work with specific entities in a particular field, while machine learning or deep learning is better for identifying more general entities.

NER has changed how different businesses and industries operate by efficiently processing large datasets. Here are some key named entity recognition use cases.

News Search

News companies create online content daily. NER helps automatically identify the who, what, when, where, and why in news and other articles. It highlights key details and helps understand the context better. 

If you're searching for information about a celebrity or a particular event, NER can categorize articles based on the entities they mention. For example, if you want to find news about Mark Zuckerberg, an NER-powered platform can suggest articles that mention “Mark Zuckerberg” or “Facebook,” even if these terms aren't in the headlines.

Chatbots

Chatbots use NER to understand user questions. By identifying key information, like names or places, they can accurately respond. For example, if a person asks a chatbot to find a fast casual restaurant in the City Hall Park, NER helps the chatbot identify entities such as the type of food (fast casual), the type of place (restaurants), and the location (City Hall Park).

Scientific research

An online journal or publication platform hosts millions of research papers and academic articles. With possibly hundreds of papers on the same topic, organizing this information can be quite challenging. For example, with about 100,000 publications on Machine Learning, tagging them by key topics makes it easy to find articles on how two-layer neural networks learn.

Legal documentation

Legal documents, especially contracts, provide important information about the parties and specifics like duration and scope. Tracking these details is a must to manage contracts and avoid accidental renewals. NER automatically finds key details like names and dates in long documents.

Pharmaceuticals and healthcare

The healthcare system has about 80% of unstructured medical data. NER helps identify and organize medical information like treatments, drugs, diseases, tests, and medication names in different documents. For example, tagging promotional materials makes it easier for medical representatives to find and share the most relevant information with healthcare professionals.

Cybersecurity

NER helps companies detect potential threats in network logs and other security data. The tool detects suspicious URLs, IP addresses, filenames, and usernames. This makes the network more secure and helps with safety investigations.

Finance

In finance, NER helps identify trends and improve risk assessments. It not only manages financial data like loans and earnings reports, but also analyzes company mentions on social media. This also helps to track events that might affect stock prices.

Social Media

Businesses use NER in social media analysis because it helps them find mentions of their brands, products, or competitors in conversations. This information helps improve brand reputation and create more targeted marketing strategies.

Named Entity Recognition lets systems understand the context of words. For example, it allows a search engine to know if “Apple” means the company or the fruit, depending on the sentence.

AI-driven solutions like chatbots and virtual assistants also use NER to specify key entities in user questions, like names, places, or dates. So they can give more precise answers.

To understand how NER works, let's look at an example where it identifies entities like people, organizations, locations, and dates in a text. Consider this passage:

Martin Eberhard and Marc Tarpenning founded Tesla Motors in July 2003, in San Carlos, California, U.S.

With NER, we would identify these words:

• Person: Martin Eberhard, Marc Tarpenning;
• Organization: Tesla Motors;
• Location: San Carlos, California;
• Date: July 1, 2003.

NER identifies the specific entities that interest you. You define these named entity recognition types and then find the matching words in the text for each category.

Implementing NER involves 8 key steps. Let's go through them so you can better understand how the process works.

Step #1: Setting Goals

Decide which entities to recognize, like people and places, and how you'll use NER, such as for extracting information.

Step #2: Data preparation

Start by collecting the data with the entities you need. Use tools like SpaCy Prodigy or datasets like CoNLL-03 to tag them. Next, clean and preprocess the data to fix any issues with punctuation or special characters.

Step #3: Deciding on Approach

Choose from different methods like:

• Rule-based approaches that use set rules for certain tasks;
• Machine Learning techniques, such as Conditional Random Fields (CRFs);
• Deep learning tools.

Step #4: Building the Model

Pick a library like SpaCy or Transformers. You can train a model from the beginning or adjust an existing one for your special needs.

Step #5: Model Assessment

Measure how well your model performs using metrics like precision, recall, and F1 score. Also, test it on various datasets to ensure it works properly with new data.

Step #6: Deployment and Integration

Integrate the NER model into your application and make sure it works with other tools and processes.

Step #7: Maintenance

Regularly check the model performance in real-world situations and update it as needed.

Step #8: Solving Challenges

In this last phase, manage any uncertainties and differences in identifying entities. You can adjust the model to fit the language and requirements of your field.

Using an API can make it much easier to set up named entity recognition software. These APIs are available online or as local tools that offer NER features. For example, Stanford Named Entity Recognizer is a popular Java tool used for extracting entities. It uses Conditional Random Fields (CRF) and comes with a pre-trained named entity recognition model to identify entities.

If you’re interested in how to do named entity recognition in Python, the Natural Language Toolkit (NLTK) is a helpful open-source tool for processing human language data. It's easy to use and works with over 100 pre-trained large language models for named entity recognition. NLTK offers tools for tasks like named entity recognition classification, stemming, tokenization, parsing, tagging, and understanding meaning. It includes a named entity recognizer (`ne_chunk`) and can be used with the Stanford NER in Python.

Let's check out some predictions for the future of Named Entity Recognition (NER).

First, experts believe pairing named entity recognition with knowledge graphs, large databases that show how different entities are connected, will improve its performance. This could make NER more precise and help discover new entities more easily.

Next, named entity recognition could play a more significant role in voice assistants and chatbots, like ChatGPT. Recent stats show that 67% of clients use chatbots for customer support, while chatbots manage 64% of routine requests. 

As these technologies become more popular, it's important to identify specific parts of speech or text to understand what users want and give the right response. If a customer asks the chatbot, "I lost my phone in London yesterday. Can you help me block my number?" a NER system will identify a phone as a PRODUCT, London as a LOCATION, and yesterday as a DATE. 

A chatbot will be able to quickly share location services and temporarily block a customer's number. This will make support service faster and more efficient.

Finally, as big data, artificial intelligence, and the Internet of Things expand, companies will use named entity recognition for cybersecurity and fraud detection. NER can interpret large amounts of data to find potential threats, which helps protect people and companies from cyberattacks. 

A study from the Czech Institute of Informatics showed that named entity recognition can help reduce phishing emails. They tested two ways to detect phishing using NER with live emails from Email.cz. The first method used a combination of NER and latent Dirichlet allocation (LDA) to find features for a random forest classifier. This method scored 100% on a public test set.

We hope this introduction to named entity recognition helped you better understand its basics. Various fields now use NER natural language processing:

• Businesses and researchers analyze large volumes of data to gain insights;
• Healthcare professionals extract important details from medical records;
• Financial experts gather data from news and reports, etc.

For NER systems to work effectively, they need high-quality data and proper training. With the right named entity recognition implementation, these systems can accurately identify entities and help businesses make smarter decisions.

SapientPro offers NLP development services and consulting to create AI models that understand human language. Our solutions can analyze large text volumes, understand idioms, fix spelling errors, and interpret complex phrases.

We also offer custom NLP solutions for your needs, helping you create applications that address language challenges and support your business goals. Contact us!