Understanding NLP: What Is an Entity?

Written by Coursera Staff • Updated on

Learn what entities are within natural language processing and how these tools can strengthen your professional portfolio. Plus, discover which skills to focus on to build your basics.

[Featured Image] A businesswoman sits at a laptop and works on an app that utilizes entity extraction and NLP.

Entities reference specific pieces of information for easier identification within natural language processing (NLP) algorithms. This is a key concept in artificial intelligence (AI), a growing and in-demand field quickly integrating into many professional areas. 

Within AI, natural language processing is one of the most sought-after skills. Building knowledge in this area can help you stand out and provide valuable insights to your organization or business. As you learn more about NLP, understanding key concepts, such as entities, can help you strengthen your foundation to develop more advanced skills. This article explores what entities are, types of entities, why NLP is valuable, and the skills you can build in this field.

What is an entity?

Entities, in the context of AI and natural language processing, are specific pieces of information or objects within a text that carry particular significance. These can be real-world entities like names of people, places, organizations, or dates. If you have specific values you are looking for, they can also be custom-defined entities tailored to your particular application, such as product names, technical terms, or domain-specific concepts.

Types of entities

You can group entities according to the type of information they reference. This can help you stay organized when extracting information from bodies of text and information. Some categories of entities you might use include:

  • Named entities: These include names of people, organizations, locations, and dates. You can have specific identifiers within this, such as person names or person occupations.

  • Custom entities: These are entities specific to a particular application or domain, such as product names, medical terms, or technical jargon.

  • Temporal entities: These are entities related to time, such as dates, times, and durations.

  • Product entities: Names of products might be grouped together into product entities.

  • Location entities: These entities categorize or classify items based on location indicators, such as state codes.

Recognizing entities

With learning about entities, understanding types, entries, and synonyms is important. You can consider entity types as categories for the information you want to extract. For instance, “subject” could be an entity type. Under each entity type, you find entity entries, which are words or phrases that represent your types. In this example, “math,” “science,” and “literature” could be entity entries under “subject.”

You also need to take into account entity reference value and synonyms. Some entity entries may have multiple equivalent words or phrases. You specify one reference value and one or more synonyms, which can aid in recognizing varied user inputs that mean the same thing. For example, you might ensure that “math,” “maths,” and “mathematics” are all recognized as “math.”

What is natural language processing?

Natural language processing is a branch of AI designed to help computers understand and communicate with humans. This type of technology helps computers interact with you in ways like reading and understanding your emails, helping you find information on the internet, or even chatting with you and answering questions. The technology lets computers comprehend, interpret, and use human language. 

You likely see the results of NLP and entity recognition in daily life without realizing it. You see NLP in action when you use voice commands with your smart speaker, get language translations, or even when your phone predicts the next word you’ll type. Another way you may see NLP used is in chatbots like Siri and Alexa, which enable natural conversations with users. In this scenario, entities help identify key elements of user queries and provide relevant responses. More examples include:

  • Search engines: Search engines like Google track search behavior and intent with NLP to provide the most relevant results.

  • Language translation: Language translation through apps uses NLP programs.

  • Sentiment analysis: Using NLP, businesses can gauge customer sentiments from reviews and social media.

  • Health care: NLP helps extract valuable insights from medical records and clinical notes.

  • Finance: NLP can analyze online information for stock market predictions.

  • Content generation: Language models (such as ChatGPT) use NLP to generate human-like responses to user questions.

What is named entity recognition (NER)?

Named entity recognition (NER) is a tool designed to help AI systems identify entities effectively. It involves algorithms and models trained to recognize predefined entity categories or tags within text, which helps to ensure AI models don’t overlook valuable information.

NER approaches

When building an NER model, you can choose several methods. Here are the three major approaches to NER.

Rule-based approaches 

Rule-based approaches start with linguistic rules that govern a language’s structure. These rules help identify entities within the text using structural and grammatical characteristics. However, while rule-based approaches can be effective, they are often time-consuming to create and may perform differently depending on the domain.

Machine-learning approaches 

You can also use machine learning to train AI-driven models using labeled data sets. Techniques range from traditional machine learning methods like hidden Markov models and support vector machines to more sophisticated deep learning approaches, such as recurrent neural networks (RNNs) and transformers. This approach generalizes well to unseen data but needs a substantial amount of labeled training data and can be computationally intensive.

Dictionary-based approaches

Dictionary-based techniques use predefined dictionaries or lists of words, phrases, or patterns to identify named entities within text data. The algorithm then looks for the specific entries. While this can be a simpler approach than others, new words or entries can make the approach less effective in some cases.

Skills to develop for NLP

Building your toolkit related to machine learning and artificial intelligence is a good place to start if you want to strengthen your foundational skills to learn more about natural language processing. Human and technical skills are valuable when learning skills related to these fields. Some human skills to focus on are:

  • Teamwork: In data science and machine learning, computer software teams often work together to build the most effective models.

  • Communication: Clear communication is an important strength in this field. You will need to be able to detail your findings in ways that are easy for people without technical knowledge to understand. 

  • Adaptability: Regarding digital work, adaptability can help you evolve with the changing landscape and stay comfortably up to date with the trends. AI and machine learning evolve fast, and being adaptable to new techniques is vital.

You can also focus on technical skills related to NLP. Some individual areas to focus on include:

  • Keyword extraction

  • Text processing

  • Tokenization

  • Emotion and sentiment analysis

  • Speech tagging

  • Language modeling

Learn more on Coursera.

You can develop your NLP skill set and learn more about concepts related to entities by taking formal courses on Coursera. Top universities and organizations lead these courses and provide in-depth explanations of beginner and advanced concepts. For a comprehensive overview, consider completing the Deep Learning Specialization, offered by DeepLearning.AI.

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