Entity extraction, also called entity identification or named entity recognition, is a text analysis technique that uses machine learning to identify and extract important entities from a text. Entities include people’s names, organizations, addresses, monetary values, and more.
Here is a simple explanation of how entity extraction works:
You can get started with entity extraction right away by using SaaS extractor APIs. Most are low-code and cloud-based, so you don’t need to spend a lot of time on setup. Text analysis tools, like this free named entity extractor, can save hundreds of employee hours pulling only the information you need from news reports, customer service tickets, emails, and more.
Request a demo from MonkeyLearn to learn more about our entity extractors and API.
Compare the best named entity recognition and extraction tools on the market to see what’s best for you.
Best for: Organizations of all sizes in tech, retail, and e-commerce that want easy-to-implement APIs for all manner of text extraction and text analysis.
MonkeyLearn offers APIs in all major programming languages, with setup that requires only a few lines of code to return a JSON file with your extracted entities. The interface is user-friendly for pre-trained extractors and text analyzers. Or you can build a custom extractor in just a few steps.
Advanced natural language processing (NLP) with deep machine learning allows you to analyze text just as a human would to save time and increase accuracy. And SaaS APIs mean you don’t need years of computer science experience to set up integrations with Google Sheets, Excel, Zapier, Zendesk, and more.
You can try out the name extractor, company extractor, and location extractor right now right in your browser. Or check out the named entity recognition blog post to learn how to build your own.
Best for: Companies that want a managed service and APIs to integrate with Google apps and Google Cloud Storage.
Google Cloud NLP offers their Natural Language API for pre-trained tools. Or, if you’d prefer to train your tools to the vocabulary of your field, the AutoML Natural Language API is versatile for all manner of text extraction and analysis.
The APIs integrate simply with Gmail, Google Sheets, and other Google apps, but can require more advanced coding if used with third party apps.
Best for: Businesses that require easy integration of mostly fixed APIs.
AYLIEN has three API plug-ins in seven major programming languages for quick access to powerful machine learning text analysis.
Their News API offers access to thousands of news sources from around the world to search and extract entities (and other data) in real time.
The Text Analysis API allows you to perform entity extraction and dozens of other text analysis on documents, social media platforms, customer surveys, and more.
Finally, you can build your own extractors (and more) directly in your browser, with the Text Analysis Platform (TAP).
Best for: Large companies, with internal engineering capabilities, that need to analyze huge datasets.
IBM Watson is an extremely fast-performing, multi-cloud platform that offers pre-built tools, including speech-to-text – impressive software that can automatically analyze phone conversations and recorded audio.
Watson Natural Language Understanding offers deep learning AI that can build extraction models with CSV files to extract entities or keywords. And with a bit of experience, you can build much more advanced models. All of their functions are available through APIs, although a fairly high level of coding experience is required.
Best for: Companies that want a managed service with pre-trained tools.
Amazon Comprehend has a number of pre-built tools that are trained to dozens of fields, so they can be easy to plug in and start using right away. As a supervised service, there is no need for in-house servers.
Their APIs integrate smoothly into existing applications, especially if you already use Amazon’s cloud to some extent. And you can increase extraction accuracy with just a little extra training work.
Comprehend’s Medical Named Entity and Relationship Extraction (NERe) has become one of the most trusted text analysis tools for extracting information from medical records and trials: medication, condition, test results, and procedures. It can be extremely helpful for comparing patient information to analyze and tweak diagnoses.
Best for: Companies that want easy-to-use APIs that don’t require AI expertise – especially in legal fields.
Cortical.io offers text extraction and NLU solutions based on a neuroscience concept called Semantic Folding. This is used to create “semantic fingerprints” that represent individual words and the meaning of a text as a whole. Semantic fingerprints visualize text data to show how word clusters relate to each other.
Cortical.io’s Contract Intelligence tool is specially designed for legal analysis to perform semantic searches, convert scanned documents, and assist with and improve with annotation.
Entity extraction, as well as more advanced text analysis software, can be a clear benefit for any type of business. Machine learning tools save time and money, and once they’re properly trained, you don’t need to worry about accuracy or missed data.
With API integration, you can set these solutions up to perform constantly and automatically. It’s just a matter of deciding what’s right for your business.
Request a demo from MonkeyLearn to help you make a final decision.
June 11th, 2020