In this article, we will introduce our top 10 Named Entity Recognition (NER) APIs, how to choose and access the right engine according to your data.
What is Named Entity Recognition (NER)?
What does NER do?
Named Entity Recognition (NER) refers to a process of scanning a sentence or piece of text for entities that can be further categorized as names, organizations, locations, quantities, monetary values, percentages, etc.
NER API can help organizations better understand customer feedback, analyze trends in large amounts of text data, and extract valuable insights from unstructured text.
A brief history of NER methods
The history of Named Entity Recognition (NER) methods can be traced back to the Message Understanding Conferences (MUC) held in the 1990s in the US. At that time, MUC focused on Information Extraction (IE) research to extract structured information about company activities and defense-related activities from unstructured text sources such as newspaper articles. At that time, the number of entity categories was limited to 7 or 10, and NER taggers for annotating entities in text were created through hand-made dictionaries and rules or some supervised learning techniques.
Over the years, there has been a shift towards supervised learning techniques like Decision Trees and Support Vector Machines, which have become the dominant NER technology. With the advancement in NLP and machine learning, NER has evolved to cover a wider range of entity categories and improved accuracy in recognizing entities in text. Additionally, NER has been integrated into various NLP applications, such as sentiment analysis and text summarization, to extract valuable information from unstructured text data.
Top 10 Named Entity Recognition (NER) APIs
1. AWS - Available on Eden AI
AWS’s NER solution is popular for its high accuracy and customization options. The API can be trained to recognize specific domains and languages, and it can integrate with other AWS tools seamlessly for further analysis and processing. Additionally, Amazon's robust security and compliance measures help ensure its scalability and reliability.
2. Google Cloud - Available on Eden AI
The API supports a wide range of languages, including English, Spanish, French, German, Chinese, and more. It can also identify entities with high accuracy and can recognize relationships between them, providing additional context to the extracted information.
3. IBM - Available on Eden AI
IBM Watson offers a highly customizable and feature-rich NER solution for entity recognition, with the ability to handle multiple languages and recognize entities in a variety of contexts.
4. Lettria - Available on Eden AI
Lettria's NER API offers a balance of accuracy and processing speed, which makes it a suitable choice for a wide range of NLP-related applications. The company also provides the ability to fine-tune its NER API for specific use cases, allowing for greater customization. Moreover, the API has a straightforward RESTful interface, making it easy to integrate into existing applications.
5. Microsoft Azure - Available on Eden AI
Microsoft offers NER API services as part of its Microsoft Azure Cognitive Services suite. The API is hosted on Microsoft Azure, providing a scalable and reliable infrastructure, and offers easy integration through SDKs and APIs. Additionally, the API supports multiple languages, supporting its use in a variety of global applications.
6. MonkeyLearn
Monkey Learn provides an advanced machine learning platform that extracts and identifies entities from unstructured text data. The API is highly customizable, allowing users to train the model to recognize entities specific to their industry or use case. It boasts a high level of accuracy and supports multiple languages for the API. In addition, Monkey Learn also provides detailed analytics and reporting, making it easy for users to track the performance of their NER models and identify areas for improvement.
7. Neural Space - Available on Eden AI
Neural Space's NER API offers customization and high accuracy, making it an appropriate choice for organizations that need to process textual data in specific domains or languages. In addition, its multilingual support and ease of integration allow flexibility for a wide range of use cases.
8. OneAI - Available on Eden AI
OneAI's NER API offers a combination of high accuracy, multilingual support, scalability and ease of integration, making it an appropriate choice for organizations that need to process large amounts of text data in various languages.
9. Repustate
Thanks to their deep learning algorithms that have been trained on large datasets, Repustate's NER API can ensure accuracy and precision. They also offer multilingual support and a highly customizable API, allowing developers to fine-tune the API's parameters to meet their specific needs.
10. TextRazor
Text Razor’s NER API has the ability to recognize entities in context, taking into account the surrounding words and phrases to identify the correct entity type. The platform also supports multiple languages and has a high level of accuracy, thanks to its advanced machine learning algorithms. In addition, the platform offers real-time processing and can handle large volumes of data
spaCy (Bonus - Open Source)
spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython. spaCy comes with pretrained pipelines and currently supports tokenization and training for 60+ languages. It features state-of-the-art speed and neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pre-trained transformers like BERT, as well as a production-ready training system and easy model packaging, deployment and workflow management. spaCy is commercial open-source software, released under the MIT license.
Some NER API use cases
You can use NER in numerous fields. Here are some examples of common use cases:
Healthcare: extract medical terms, diseases, and symptoms from patient notes and electronic health records, helping to improve patient care and identify potential health risks.
Finance: extract financial entities such as stock symbols, company names, and financial events from news articles, social media, and other sources, helping traders and investors make informed decisions.
Marketing: extract brand names, product names, and other relevant entities from social media and customer feedback, enabling businesses to gain insights into customer sentiment and identify areas for improvement.
Legal: extract legal terms, case citations, and other relevant entities from legal documents, helping lawyers and law firms streamline their document review processes.
E-commerce: extract product names, brand names, and other relevant entities from customer reviews and product descriptions, providing valuable insights into customer preferences and helping businesses improve their product offerings.
These are just a few examples of NER APIs uses case. This technology can be leveraged in diverse applications to extract relevant information for further purposes.
Why choose Eden AI to manage your APIs
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, 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:
Fallback provider is the ABCs
You need to set up a provider API that is requested if and only if the main NER API does not perform well (or is down). You can use confidence score returned or other methods to check provider accuracy.
Performance optimization.
After the testing phase, you will be able to build a mapping of providers performance based on the criteria you have chosen (languages, fields, etc.). Each data that you need to process will then be sent to the best NER API.
Cost - Performance ratio optimization.
You can choose the cheapest NER provider that performs well for your data.
Combine multiple AI APIs.
This approach is required if you look for extremely high accuracy. The combination leads to higher costs but allows your AI service to be safe and accurate because NER APIs will validate and invalidate each other for each piece of data.
How Eden AI can help you?
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.
Centralized and fully monitored billing on Eden AI for all NER APIs
Unified API for all providers: simple and standard to use, quick switch between providers, access to the specific features of each provider
Standardized response format: the JSON output format is the same for all suppliers thanks to Eden AI's standardization work. The response elements are also standardized thanks to Eden AI's powerful matching algorithms.
The best Artificial Intelligence APIs in the market are available: big cloud providers (Google, AWS, Microsoft, and more specialized engines)
Data protection: Eden AI will not store or use any data. Possibility to filter to use only GDPR engines.
You can see Eden AI documentation here.
Next step in your project
The Eden AI team can help you with your NER integration project. This can be done by :
Organizing a product demo and a discussion to better understand your needs. You can book a time slot here: Contact
By testing the public version of Eden AI for free: however, not all providers are available on this version. Some are only available on the Enterprise version.
By benefiting from the support and advice of a team of experts to find the optimal combination of providers according to the specifics of your needs
Having the possibility to integrate on a third-party platform: we can quickly develop connectors