This data sheet offers a general overview of what we do, how we do it, and who we do it for, including our NLP features, products, deployment options, industries and applications, and more.
Learn how we solve precision and accuracy issues in text analytics by tuning the system or training a machine learning "micromodel" to solve particularly tricky challenges.
Semantria Storage & Visualization adds web-based data storage, document management, and data visualization tools to your Lexalytics Intelligence Platform solution.
This solution profile explains how RPA vendors can solve more and broader enterprise use cases by integrating a complete, customizable text analytics solution into their platform, and why Lexalytics is the perfect technical partner to add that next level of analytical differentiation.
Learn how Lexalytics combines our semi-structured data parser with NLP to unlock the full value of contracts, medical billing code updates, and other financial, medical and legal documents.
Salience is a set of on-premise software libraries that adds powerful, customizable text analytics and natural language processing (NLP) to your own enterprise analytics stack or data analytics product.
Semantria wraps the text analytics and natural language processing features of our Salience engine into a flexible, easy-to-deploy RESTful API with graphical configuration tools.
Learn how Lexalytics, a company with more than a decade of experience in the space, uses machine learning to solve tricky problems in natural language processing and text analytics.
Weâ€™ve developed a particular philosophy around how to customize a text analytics system to better fit your business. This white paper explains why you should "Tune First, Then Train".
This white paper explains how we break down sentences into n-grams and noun phrases and evaluate the themes and facets within to help you perform contextual analysis.
Large-scale "AI for regulatory compliance" systems often fall short. Here's how Lexalytics creates time savings and cost reductions by building targeted, semi-custom applications.
"Build or Buy" for NLP and text analytics means choosing between building from open source, licensing a basic cloud API, or customizing an NLP platform. This paper explains that choice in depth, including what to expect if you choose to build, and how to choose the best text analytics and NLP provider.
Sentiment analysis is the process of determining whether a piece of writing is positive, negative or neutral. This paper dives deep into how it works, including the role of machine learning and AI.
Categorization of text documents means sorting them into groups. This paper explains how Lexalytics combines natural language processing (NLP) and machine learning to automatically classify customer reviews, support tickets, or any other type of text document based on their contents.
Who are they talking about? Learn how we extract people, places, dates, companies, products, jobs, and titles from text documents.
With so much hype and so many options out there, how can you be sure you're choosing the right AI partner? Start by using these 4 questions to guide your search.
Text is one of the most important modes of human communication. Seth Redmore explains how starting from the question, rather from data, helps you truly listen to your customers, partners, competitors, and employees.
This white paper is a useful primer on the fundamentals and mechanisms of Natural Language Processing (NLP).
Text analytics, also called text mining, has countless applications. Businesses are taking advantage of text analytics to update their service offerings, improve compliance, get ahead of PR disasters, and more. Here are 5 examples of […]
Artificial intelligence has rendered HIPAA obsolete. As AI in healthcare becomes commonplace, data privacy, security and ethical issues are growing. We explored the uses of AI in healthcare, the ethical issues involved, and why experts are raising flags about healthcare data storage and data security practices.
Every year, we like to consider what happened in the last year in AI, ML and NLP, and prognosticate on what might happen in the next 12 months. And while 2020 was a doozy of […]
From Tokenization to PoS Tagging and beyond, this high-level overview explains the basic functions of text analytics and natural language processing.
Paul Barba explains the role of machine learning and AI in natural language processing, how to apply machine learning to solve problems in natural language processing and text analytics, and why a hybrid ML-NLP approach is best.