This article is brought to you by the Eden AI team. We allow you to test and use in production a large number of AI engines from different providers directly through our API and platform. You are a solution provider and want to integrate Eden AI, contact us at : email@example.com
In this article, we are going to see how we can easily integrate a Named Entity Recognition (NER) engine in your project and how to choose and access the right engine according to your data.
In Natural language processing, Named Entity Recognition (NER) is a process where a sentence or a chunk of text is parsed through to find entities that can be put under categories like names, organizations, locations, quantities, monetary values, percentages, etc. With named entity recognition, you can extract key information to understand what a text is about, or merely use it to collect important information to store in a database.
The term “Named Entity (NE)” was born in the Message Understanding Conferences
(MUC) which influenced IE research in the U.S. in the 1990’s. At that time, MUC focused on Information Extraction tasks where structured information of company activities and defense related activities is extracted from unstructured text, such as newspaper articles. Outside the U.S., there have been several evaluation-based projects for NE. Around this time, the number of categories is limited to 7 to 10, and the NE taggers, automatic annotation systems for NE entities in unstructured text, are based on dictionaries and rules which were made by hand or some supervised learning technique. More recent and currently dominating technology is the supervised learning techniques like Decision Tree, Support Vector Machine, etc.
You can use Named Entity Recognition in numerous fields, here are some examples of common use cases:
When you need a NER engine, you have 2 options:
The only way you have to select the right provider is to benchmark different providers’ engines with your data and choose the best OR combine different providers’ engines results. You can also compare prices if the price is one of your priorities, as well as you can do for rapidity.
This method is the best in terms of performance and optimization but it presents many inconveniences:
Here is where Eden AI becomes very useful. You just have to subscribe and create an Eden AI account, and you have access to many providers engines for many technologies including NER. The platform allows you to benchmark and visualize results from different engines, and also allows you to have centralized cost for the use of different providers.
Eden AI provides the same easy to use API with the same documentation for every technology. You can use the Eden AI API to call NER engines with a provider as a simple parameter. With only a few lines, you can set up your project in production:
Here is the code in Python (GitHub repo) that allows to test Eden AI for face detection:
Eden AI also allows you to compare these engines directly on the web interface without having to code:
There are numerous NER engines available on the market: it’s impossible to know all of them, to know those who provide good performance. The best way you have to integrate NER technology is the multi-cloud approach that guarantees you to reach the best performance and prices depending on your data and project. This approach seems to be complex but we simplify this for you with Eden AI which centralizes best providers APIs.
In this article, we explain how the mapping between the input language and the languages supported by the providers is performed to facilitate access to one of our AI engines.