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.
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.
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.
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.
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.
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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.
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.
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.
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.
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.
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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.
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.
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 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.
You can use NER in numerous fields. Here are some examples of common use cases:
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.
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:
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.
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.
You can choose the cheapest NER provider that performs well for your data.
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.
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.
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You can see Eden AI documentation here.
The Eden AI team can help you with your NER integration project. This can be done by :
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