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Entity Extraction Tools and Services


Entity extraction refers to a range of content processing techniques that identify and extract entities (e.g. people, names, locations, companies, dollar amounts, key initiatives, etc.) within enterprise unstructured, natural language content, such as text documents, emails, images, reports, etc. Effectively extracting entities can automate unstructured content processing tasks, enable a better understanding of your data, and deliver actionable insights.

The accuracy of entity extraction is critical, especially when it is applied to specific enterprise contexts. Without appropriate accuracy and provenance, it is difficult to provide an optimal search and analytics experience to the user and to maximize the ROI of the application.


Our entity extraction services typically follow the approaches below.

  • Rule-based matching: requires the support of dictionaries.
  • Machine learning: when the data set is large and broad, using machine learning to identify and extract entities can help automate and allow the process to be more efficient. This approach derives relationships from statistical co-occurrence within the document corpus.
  • Hybrid solutions: machine learning combined with rule-based matching for both broad coverage and high accuracy. This is typically the most-preferred approach if the content is focused on a specific, unique subject area of the business.

To get a deep dive into entity extraction and how it supports natural language processing (NLP) projects, read our blog.

Depending on your entity extraction needs, we will work with you to define the appropriate approach and customize our services to deliver the best results. This, in turn, can help you improve and optimize the performance of your search and analytics applications. 


  • Aspire Content Processing – a high-performing content processing platform that supports unstructured data preparation, from acquiring, parsing, cleansing, and normalization, to filtering and semantic analysis. The processed data can then be used in search and analytics projects at any scale.
  • Connectors – over 30 secure connectors to help you gather data from multiple enterprise applications.
  • Saga Natural Language Understanding (NLU) – enables non-data scientists to create and maintain powerful, flexible, tested, and scalable enterprise language models for user interaction and document understanding. It incorporates many language modeling techniques and machine learning into a single, user-friendly semantic framework to handle a wide variety of natural language processing use cases.

Contact us to learn more about our entity extraction services and tools and how they can work for your use case.