
Solutions
Vision AI
Use Cases
Resources
Popular Guides
Key Resources
Company
Company Info
Scale your text and audio annotation with a team skilled in understanding and interpreting complex, nuanced language.
Machine learning models are advancing in their ability to read and understand text and audio but still struggle to understand the complexities of language. Our experienced data analyst teams understand the nuance of your business and the subtleties in language required to accurately tag the text you need to train your NLP applications. Whether you’re training a chatbot, legal contract review application, or financial analysis algorithm, our NLP services provide the high-quality data you need to make the models powering your analytical or speech-based applications more accurate.
Our data analysts combine your business context with their understanding of language, syntax, and sentence structure to accurately parse and tag text according to your specifications. We can extract meaning from raw audio and text data to advance your NLP project.
From information extraction to sentiment analysis, we can help you unlock the hidden insights contained within written text and verbal language, powering your NLP algorithms and machine learning models.
Deriving and determining structure from text to enhance and augment the data.
Establishing clarity on features of interest in the text by eliminating noise (distracting text) from the data to be analyzed.
Creating a hierarchical structure of relationships among concepts in text and specifying the terms to be used to refer to each.
Identifying aspects of language present in the text, such as part-of-speech tagging.
Placing text into organized groups and labeling it, based on features of interest.
Also called text classification and text tagging.
Identifying words that signal a user’s intent, often used to determine actions to take based on users’ responses.
Also known as intent detection and intent classification.
Analyzing a string of symbols in text, conforming to the rules of a formal grammar.
Also known as syntactic analysis.
Extracting meaning from text by identifying recurrent themes or topics.
Also known as topic labeling.
Identifying text that represents specific entities, such as people, places, or organizations.
Also known as named entity recognition, entity chunking, and entity extraction.
Analyzing the context and text structure to accurately distinguish the meaning of words that have more than one definition. Also called context analysis.
Extracting meaning from text to determine its emotion or sentiment. Also called opinion mining.
Creating a brief description that includes the most important and relevant information contained in the text.
Our vetted, managed teams have served hundreds of clients across thousands of use cases that range from simple to complex.
Our proven processes securely and quickly deliver accurate data and are designed to scale and change with your needs.
Contract terms that include all you need to succeed and predictable hourly pricing that removes the risk of hidden costs.
I have a partner that I can go to for scaling up my labeling efforts. I can churn out new models because I can get the data prepared quickly.
True Lark turned to CloudFactory to help them scale data tagging for development of a robust customer communication solution.
Heretik wanted to make legal contract review less tedious so they created an AI-fueled solution. Learn how CloudFactory helped bring their software to life.
Learn how CloudFactory helped Heretik train their AI models and disrupt the legal industry by streamlining the contract review process.
We’d love the opportunity to answer your questions or learn more about your project. Let us know how we can help.
Turn online information into actionable insights with machine learning, gaining a deeper understanding of brand reputation, product reviews, article intent, and more.
The age of remote learning is here, and with it comes the need for technology that can support robust online or hybrid education experiences.
Chatbots and virtual assistants aren’t new, but they are often limited in the kinds of questions they can answer and tasks they can manage.
The poor utilization of people in the AI lifecycle can be costly and yield low-quality data and poor model performance. Learn how to strategically deploy a scalable human-in-the-loop workforce to build and maintain high-performing machine learning models, from proof of concept to production.
Keith McCormick and Usama Fayyad discuss autonomous vehicles, deep learning, NLP, and the future of data science in this on-demand webinar.
Are you training machine learning algorithms or planning to scale your team's training data operations? Here are some tips to ensure your people, processes, and tools produce quality training data.
NLP is one of the most difficult AI applications to develop and maintain. When you outsource data labeling, make sure you choose the right team.
© 2010-2023 CloudFactory Limited | Privacy Policy