Named Entity Recognition typically involves analyzing unstructured text data and categorizing named entities within text, such as people, organizations, locations, dates, and other predefined categories.
The technology uses machine learning and natural language processing (NLP) techniques to recognize patterns, context, and linguistic features, to determine the boundaries and types of named entities. The output can be used for various purposes, such as information extraction, content analysis, search, recommendation systems, and more.
You can use NER in numerous fields, here are some examples of common use cases:
These are just a few examples of NER APIs’s use cases. This technology can be applied in different fields to extract relevant information and enhance their applications or systems.
While comparing NER APIs, it is crucial to consider different aspects, among others, cost security and privacy. NER experts at Eden AI tested, compared, and used many NER APIs of the market. Here are some actors that perform well (in alphabetical order):
Leveraging state-of-the-art deep learning models and advanced NLP techniques, Allganize.ai's NER API delivers highly precise results, accurately recognizing and classifying various types of entities. Its extensive language support and multilingual capabilities enable efficient entity extraction across various languages.
With its exceptional precision and versatile customization features, AWS's NER solution has gained significant popularity. The API can be effectively trained to identify distinct domains and languages, seamlessly integrating with various AWS tools to enable advanced analysis and processing. Moreover, Amazon's robust security and compliance measures provide a strong foundation for scalability and reliability.
With its broad language support, the API can accurately detect addresses and phone numbers based on the chosen language. Its exceptional accuracy enables precise identification of entities while establishing meaningful connections among them. This enriches the extracted information with valuable contextual insights, enhancing its overall quality.
IBM Watson presents a remarkably customizable and feature-rich NER solution tailored for entity recognition. It boasts the capability to efficiently handle diverse languages and identify entities across various contexts with utmost accuracy.
Lettria's NER API strikes a harmonious balance between accuracy and processing speed, making it a fitting choice for a wide range of NLP-related applications. The company additionally offers the flexibility to fine-tune the NER API for specific use cases, enabling greater customization. Furthermore, the API boasts a user-friendly RESTful interface, simplifying its seamless integration into existing applications.
Azure provides NER API services as part of the Microsoft Azure Cognitive Services suite. These services are hosted on the robust Microsoft Azure infrastructure, offering scalability and reliability. Integration with the NER API is made effortless through the availability of comprehensive SDKs and APIs. Moreover, the API supports multiple languages, making it suitable for a wide range of global applications.
Neural Space's NER API delivers exceptional customization capabilities and remarkable accuracy, making it an ideal solution for organizations requiring precise textual data processing in specific domains or languages. Furthermore, the API supports over 36 different entities 87 languages, providing flexibility for a diverse range of use cases.
NLP Cloud's NER API offers advanced entity recognition with customization options, multilingual support, and pre-trained models for accurate extraction of names, locations, organizations, and more. Its user-friendly interface enables seamless integration into existing applications.
OneAI's NER API presents a compelling blend of high accuracy, multilingual support, scalability, and seamless integration. This makes the API a suitable choice for organizations dealing with substantial volumes of text data across different languages, catering to their diverse processing needs effectively.
Open AI’s technology leverages cutting-edge machine learning models to accurately identify and extract entities from textual data. Users have the flexibility to customize the API's behavior according to their specific requirements, enabling fine-tuning for domain-specific entity recognition. The API also offers multilingual support, allowing users to process text in different languages.
For all companies who use NER in their software: cost and performance are real concerns. The NER market is quite dense and all those providers have their benefits and weaknesses.
Performances of NER vary according to the specificity of data used by each AI engine for their model training. This means that some NER may perform great for some languages but won’t necessarily for others.
NER APIs perform differently depending on the language of the text, and some providers are specialized in specific languages. Different specificities exist:
Some NER APIs trained their engine with specific data. This means that some NER APIs will perform better for medical documents, while others will perform better on legal contracts.
Companies and developers from a wide range of industries (Social Media, Retail, Health, Finances, Law, etc.) use Eden AI’s unique API to easily integrate NER tasks in their cloud-based applications, without having to build their own solutions.
Eden AI offers multiple AI APIs on its platform amongst several technologies: Text-to-Speech, Language Detection, Sentiment Analysis API, Summarization, Question Answering, Data Anonymization, Speech Recognition, and so forth.
We want our users to have access to multiple NER engines and manage them in one place so they can reach high performance, optimize cost and cover all their needs. There are many reasons for using multiple APIs :
Eden AI has been made for multiple AI APIs use. Eden AI is the future of AI usage in companies. Eden AI allows you to call multiple AI APIs.
You can see Eden AI documentation here.
The Eden AI team can help you with your NER integration project. This can be done by :