Named Entity Recognition (NER): What It Is & How It Is Used in '24
By 2025, the global natural language processing (NLP) market is anticipated to be worth ~$43 billion, 14 times larger than it was in 2017 (Figure 1). NLP has a wide range of use cases in every industry from chatbots to document processing by enabling machines to understand and process human languages.
In this article, we will explore named entity recognition, one of the techniques that help computers extract meaningful information from texts written in human languages.
What is named entity recognition?
Named entity recognition (NER) is the process of identifying and classifying named entities presented in a text document. NER is an application of natural language processing (NLP) and its main goal is to extract relevant information from text data. It automatically classifies named entities according to predefined categories such as individuals, places, dates, etc. Named entities can also be numerical expressions like phone numbers or times.
Figure 1: Representation of NER in a given text document
For instance, the entities in Figure 1 are:
- Person: Albert Einstein
- Location: Ulm, Germany, Munich, Zurich
- Organization: Gymnasium, School
How does named entity recognition work?
- NER scans whole text and detects named entities: It detects the sentence boundaries in a given document based on capitalization rules. Identifying the sentence boundaries will assist NER in finding and extracting relevant information from the document for the next steps.
- Categorize entities into predefined categories: In order to tag words or phrases, entity categories such as location, people, event, time, organization, and so on must be clearly defined. The entity extraction model can then be trained with predefined categories so that it can identify entities such as people, places, and organizations in raw texts.
What are the use cases of named entity recognition?
- Customer Support: Companies receive a large amount of customer feedback and complaints about their team, product, or any other daily services. NER also assists businesses in classifying customer complaints. It identifies relevant entities in customer complaints and categorizes them based on team, department, product, or company branch location. These complaints are automatically routed to the appropriate department or branch. It enables companies to build an automated system that steers toward customers’ requests to the relevant support desk.
- Human resources: NER can speed up the hiring process by automatically filtering out resumes to find the appropriate candidates with the required skills. Specific skills can be used as entities for NER applications in hiring processes.
What are the main approaches to NER?
Named entity recognition has three major approaches:
- Lexicon-based approach: Lexicon-based approach uses a pre-prepared lexicon to match chunks of text with entity names. However, with this approach, NER is able to recognize new entities that are not in the lexicon. Lexicon is created by starting with a few words and then expanding them as much as possible.
- Rule-based approach: The model uses predefined rules to extract information in a given text. The system constructs rules automatically or manually.
- Machine learning-based approach: An ML model is trained on the annotated texts. Then, the pre-trained ML model is used to recognize entities from raw documents.
If you have other questions about named entity recognition, we would like to help:
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