Named entity recognition

Named entity recognition is a form of artificial intelligence (AI) using natural language processing methods to extract data from text. It enables computer systems to gain an understanding of the information contained within textual data. The extracted data can then be analyzed and categorized to make it easier to comprehend and use in subsequent data processing and analytics steps.

Discover the various applications of named entity recognition technology, along with its significance and its integral role in the advancement of AI.

What is named entity recognition?

Named entity recognition (NER) definition: Named entity recognition is a component of ​natural language processing that identifies categories in a body of text. 

This process is also known as entity extraction, chunking and identification, with computer systems extracting identifying data from textual information. The system then classifies data into categories, which are known as “named entities.”

These categories that are identified by using named entity recognition can be designated specifically for use cases, so the technology lends itself to a wide variety of uses. NER models can also improve over time, as the more data and text they analyze, the better and more precise they become in correctly and succinctly categorizing pieces of information.

The named entity recognition method can be used with ​machine learning to make processes more efficient and dynamic, and the method can also be applied to artificial intelligence-driven models. This approach allows both the named entity recognition and AI-driven model to improve over time.

How does named entity recognition work?

Named entity recognition uses predictive modeling and machine learning techniques to extract entities from text. The process involves applying algorithms based on grammar, which are trained on pre-labeled datasets to learn how to identify these entities.

Once trained, it can then analyze unstructured data, recognizing named entities with increasing accuracy as it processes more data. The more data it analyzes, the better it becomes at identifying these entities.

There are several steps to the NER process, regardless of the specific type being used. 

1. Data collection: The first step in using NER is aggregating the dataset. This aggregation is a crucial step in gathering a comprehensive dataset. Having a dataset with annotated text is an ideal place to start.
2. Data processing: Data must be formatted properly to avoid any issues later in the process. There are a few ways to format data for named entity recognition, which include pandas DataFrame, CoNLL and LayoutLM.
3. Feature extraction: This step involves identifying and extracting contextual information before analysis. It enhances the model’s ability to recognize entities accurately.
4. Model training: This is the natural next step, especially when working with machine learning or deep learning models, to provide the model with the necessary information before analyzing the data.
5. Model testing: Before running the model, it is important to test it with a smaller sample size to ensure it is set up correctly and not performing any unwanted analysis.
6. Inference: After successful testing, the model is ready to analyze unseen data and text. At this stage, the NER system should be capable of accurately identifying and categorizing named entities.

What is the history of named entity recognition?

Named entity recognition grew from natural language processing technology that has been in development since the early 20th century. As natural language processing saw rapid growth and development in the 2000s, named entity recognition as a concept was born. 

NER is a natural evolution of natural language processing, adding a process of categorizing the textual data using predefined groupings. It is still the subject of research as data scientists explore ways to pull meaning more naturally and efficiently from text using computer algorithms.

What are key types of named entity recognition?

Named entity recognition isn’t limited to machine learning alone; it encompasses four distinct types and methods.

• Machine learning systems require a large amount of training data, but they offer greater value overall. This technique can include traditional machine learning methods, such as ​​decision trees, but it can also include more complex methods such as ​neural networks. 
  A key unique selling proposition (USP) of this system is its semantic parsing. This allows the machine to better understand the text because it can be converted to something that is easier to interpret and understand, leading to a more effective output.
• Neural networks are also used in deep learning systems with NER. These networks examine the structure of sentences in the text that is being analyzed. Deep learning tends to be a more effective way to train this AI-driven model since it can handle large sets of data similarly but learn autonomously.
• Dictionary-based systems supply a wide-ranging vocabulary to the system to cross-check named entities. One issue with this system is that different spellings of named entities can cause confusion in the NER process.
• Rule-based systems essentially “translate” or create language rules so that named entity recognition is possible. Creating a set of rules based on structural and grammatical features speeds up the NER process, but creating the sets of rules needed to produce quality data can be time-consuming.

How is named entity recognition used?

The versatility of named entity recognition as an ​​artificial intelligence tool is one of its advantages. It can be applied widely across a range of industries and use cases.

In the legal sector, NER can be invaluable when having to analyze lengthy legal documents. When under a time restraint, using the NER method to pull out important names, dates and titles enhances efficiency and drives more effective, speedy ways of working in a busy industry. These benefits aren’t just restricted to the legal sector either. A number of fields benefit from this method of analyzing important documents.

Customer support is another key use case with NER. Analyzing customer queries with named entity recognition can help pinpoint customers’ issues and resolve them quickly. In these cases, named entity recognition can be used as a step above natural language processing to ensure that responses to clients and customers are precise and accurate.

NER is also a powerful tool for recommendations across a wide range of content. From suggesting another article to read on a person’s go-to news site or promoting a new series or film to watch based on the history of that person’s streaming, NER can recognize similarities and differences among users’ choices. With clear entities defined for the systems to recognize and categorize, a strict set of parameters ensures that responses and outputs are accurate and functional.