What is the Relevance of Named Entity Recognition in NLP?

Data is the most crucial element for organizations to gain a competitive advantage. However, many organizations have a hard time processing real-time data because it is usually available in unstructured formats. Therefore, businesses are adopting Named Entity Recognition (NER) techniques to quickly extract relevant data, increase their efficiency, and enhance customer service. This blog post discusses what NER is and its role in Natural Language Processing (NLP).

What is Named Entity Recognition in the Context of Natural Language Processing?

Before we get into NER, let us first understand what entity means in the language of AI and Machine Learning (ML). Entity refers to an item or an element that is useful and has some significance.

For example, a user talks to an AI chatbot to get details of its order history and types, “Show my order history of the previous month”. Here, the “order history” and “previous month” are entities that hold significance. The AI model can use this information to provide specific results. Think about entities as specific keywords. They include data such as text, place, time, number, person, or item.

What is Named Entity Recognition?

Named entity recognition is an NLP technique or method to extract an entity from a text. This technique identifies and classifies predefined categories of entities such as individual names, timestamps, locations, dates, and monetary value. Therefore, NER focuses on the semantics (deriving words, text, or speech) of a given piece of information.

NER extracts the relevant data from unstructured data sets and converts or arranges them into organized formats through systematic information retrieval. 

Some of the key components of a named entity recognition model are:

• Tokenization, which means breaking complex words or punctuations into individual elements
• The identification and classification of named categories 
• Part-of-speech tagging refers to labeling the tokens based on the part of speech such as noun, verb, adverb, or adjective
• Chunking means categorizing tokens into groups based on the part-of-speech they belong to
• Entity disambiguation involves understanding the correct meaning of an entity when two or more entities of the same name are present

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How Does Named Entity Recognition Help in Extracting Meaningful Information From Unstructured Text?

Organizations used named entity recognition for entity detection and information retrieval through the following three approaches:

1. Rule-Based Approach 

This approach involves using predefined rules or patterns for data extraction. NLP engineers define grammar rules or other structural rules that are applied to the NLP model. Therefore, the rule-based approach is divided into two categories—pattern-based rules to decode the information and context-based rules that involve understanding the meaning of words based on their usage. However, this approach is not considered the best one because it takes time to train several rules to ML models and may not extract entities efficiently.

2. Machine Learning Approach 

The machine learning approach uses statistical modeling techniques. In this approach, an AI-driven machine learning model is trained to make a featured-based representation of the textual data. It uses supervised machine learning algorithms such as conditional random fields and maximum entropy, which are complex statistical language models. The machine learning approach also uses techniques like decision trees, support vector machines, and recurrent neural networks. This approach is beneficial to extract unseen data. However, it is expensive because large amounts of labeled data are required for training purposes.

3. Hybrid Approach 

As the name suggests, the hybrid approach combines the features of rule-based and machine-learning approaches for entity detection and data extraction. It uses the rule-based methodology for quick extraction of entities and the machine-learning methodology to identify and extract complex entities. Hence, the hybrid approach can be more suitable for businesses.

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What are the Different Types of Entities That Can be Recognized Using NER?

The named entity recognition technique in NLP can identify the following categories of entities:

1. Person 

This category includes names of people such as Alex, George, and Christine.

2. Person Type

It primarily describes the job roles such as scientist, data engineer, and financial analyst.

3. Organization 

The organization entity category involves organizations or groups such as companies, financial institutions, and government organizations.

4. Event 

Event entities involve any political, historical, social, cultural events, and unnaturally and naturally occurring events.

5. Location 

NER can extract location category entities that include geographical areas, landmarks, artificial or natural structures, and geopolitical entities such as cities, regions, or states.

6. Skill

This category includes capability or expertise such as machine learning, data analytics, programming, and leadership.

7. Product 

Product category includes entities that are in the form of physical objects such as computing products or machinery.

8. Address

An entity also includes the full mailing address of an individual or organization.

9. Email

Similar to the above, email addresses of individuals or organizations are also defined as entities and can be extracted.

10. URL

Website URLs also come under the definition of entity.

11. IP

Network IP addresses of mobile phones or computer systems are also considered entities.

12. Phone Number 

Another category of entities is phone numbers.

13. Quantity 

This category of entities includes numbers and numeric quantities such as percentages, currencies, dimensions and measurements, age, and temperature.

14. Date and Time

Dates and timestamps are also entities. These include calendar dates, date and time ranges, and durations.

Some other categories of entities for named entity recognition are disease, brand, color, and design.

How Can Named Entity Recognition Enhance the Performance of AI and ML Models?

Named entity recognition significantly enhances the performance of AI and ML models by identifying target entities and extracting information. It can increase the efficiency of several automated tasks such as text summarization, answering questions, or translating text. 

NER enables machine learning models to quickly extract important information from huge volumes of unstructured data. Thus, it helps organizations make real-time decisions.

What are Some Real-World Applications of Named Entity Recognition?

Due to its ability to simplify complex data, named entity recognition has widespread usage across several industries. Its real-world applications include:

1. Customer Service 

The use of named entity recognition for AI chatbots and virtual assistants can help organizations process unstructured data quickly and efficiently. Therefore, organizations can resolve customer queries promptly and enable better automation of customer services.

2. Cybersecurity 

Since NER can easily skim through unstructured data, it can identify potential cybersecurity breaches or other suspicious activities. Therefore, it can be used in cybersecurity models.

3. Financial Services 

Financial institutions also use NER to understand customer requests and process financial transactions in real time. Moreover, since NER helps extract data quickly, organizations can leverage it to detect financial fraud or any suspicious activities.

4. Search Engines

Named entity recognition is also highly beneficial for search engines. It understands users’ search queries by focusing on relevant entities (keywords) and scanning through large databases to provide relevant information to users.

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As organizations are generating and processing huge amounts of data every second, they need efficient techniques and resources to extract valuable data insights. Therefore, the demand for NLP professionals is likely to increase in the future. Explore Emeritus’ artificial intelligence and machine learning courses to learn more about named entity recognition and natural language processing and how to leverage them.

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