The named entity recognition (NER) model is a natural language processing (NLP) application that has become the basis for certain automatic tasks, such as machine translation, information retrieval, and text anonymization.
The benefits of NER in NLP are numerous: it helps save time, simplifies processes, and provides valuable information within any industry, such as banking, finance, public administrations, publishing companies, etc.
What is the NER model?
The NER model is defined as a tool developed by means of artificial intelligence and machine learning for detecting and classifying certain words or sets of words (named entities) within a text, using previously established categories.
For example, in a set of documents, such as contracts, this model can identify the names of people or companies, and classify them using the proper names category or label.
Each model, depending on the developer and the labeled data, will perform at a certain level of quality in NER labeling. It can be trained, and its performance and accuracy improved in order to adapt and customize it for entity recognition and extraction, according to the subject matter and languages required.
The importance of using named entity recognition
Using NER is of great relevance for any sector or industry, as this tool can:
- Facilitate the understanding of a set of documents by detecting and highlighting keywords or terms.
- Help extract valuable information from documents in an easy and fast way.
- Process large volumes of information automatically and in a short amount of time.
Depending on the development of the model, it will be able to identify a certain number of NER labels or categories. For example, basic NER software can detect 3 tags: people, organizations, and places. Others have a greater capacity, with the ability to identify time, streets, currency, quantities, and nationality, among other categories.
Most NER models are general, covering all areas. But some models can extract terms in a specific field or domain, such as in science, medicine, finance, law, etc.
All these functions serve as the basis for important NLP applications and are very useful for companies in any industry, mainly for data anonymization.
During the anonymization process, a NER model has the ability to identify personal data and eliminate it automatically.
However, this is not the only application; NER and NLP are also combined for:
- Document classification. By detecting prominent entities, the model can identify the subject matter of the document and proceed with its classification.
- Text quality control. This model is very useful for detecting plagiarism and text quality because it captures both similarities and anomalies among a group of documents.
- Word or term extraction in fields with specific technical vocabulary, such as medical or financial documents.
Examples of NER usage
All these applications of NER models can be observed in multiple business scenarios. Examples include the following:
- In financial institutions, law firms or judicial administration. Every day, information must be extracted from hundreds or even thousands of documents with complex subjects. A NER model simplifies information extraction processes, reducing human error, time, and costs.
- The IT departments of organizations in highly specialized fields, such as banks, insurance companies, or law firms reap many benefits from NER models, as they facilitate the development of automated and customized solutions, according to the specific subject matter required.
- Any company or organization that collects and makes use of personal data utilizes NER models for text anonymization. For example, scientific organizations, banking or financial institutions, and educational institutions.
- In clinics and health facilities, NER models are used to extract important information from clinical reports or analyses in order to provide better patient care.
- Any company can classify applications, inquiries, concerns, and complaints through the use of NER models, and thus optimize its response times.
- Companies streamline recruitment and selection processes by using NER models to extract key information from applicants' resumes.
What types of companies offer this technology?
Companies providing this technology must be organizations that specialize in NLP, AI and deep machine learning. Consequently, they will be able to produce accurate NER models, specific to various fields and with a wide range of languages.
At Pangeanic, we are leaders in the field of NLP. We provide anonymization services and our own AI-based model for named entity recognition, with special versions for all types of industries and business niches.
Our NER model can function as an integrated part of our data anonymization service, or as a stand-alone system. It has the ability to detect terms associated with people, companies and places. In addition, it has the capacity to extend its coverage to other entities, such as age, time, family members, ID numbers, professions, positions, events, etc.
Contact us to streamline your document management processes. At Pangeanic, we offer a completely customized anonymization service and NER model to suit your company or industry.