Entity Extraction Solution
Businesses today deal with large volumes of unstructured data, comprising emails, surveys, social media comments and more. Manually processing them to drive insights is not only time-consuming but often inaccurate.
Our entity extraction accelerator teX.ai TM leverages machine learning (ML) and natural language processing (NLP) to extract meaning from text and voice data and categorizes them according to pre-defined classes or entities such as people, organization, place, monetary assets, and more. Its applications are wide-ranging and can be leveraged for text summarization, typical question-answering and to automate routine business tasks for greater efficiency.
teX.ai TM ’s entity extraction process
Our intelligent AI-powered text analytics accelerator helps with unstructured data preparation before the process starts with data extraction. The data is parsed, cleansed and normalized to make it suitable for filtering and semantic analysis.
teX.ai TM can gather data from multiple sources to help you gain a holistic view on customer feedback and derive actionable insights.
teX.ai TM allows you to build your own list of custom entities. Anything that your company defines as an entity can be identified and tagged accordingly.
Through the ensemble ML models, it can choose the most suitable model for the label provided. It understands a wide array of entities to include in your lists and can also provide context to the extracted entities.
How teX.ai TM can help
Based on your entity extraction requirements, our data science team will help in configuring the initial step and re-tune the model to meet your key requirements.
We have implemented customized text analytics solutions for our customers. Our named entity extraction solution can help with entity extraction for organizations in the banking, retail, manufacturing, education, healthcare and life sciences industries.
teX.ai TM’s entity extraction can help you achieve the following use cases.
teX.ai TM can help companies that deal with an increasing number of customer support tickets to extract relevant data from incoming tickets and route them to the respective support team to resolve the issue. This not only saves valuable time for companies but improves resolution rates and, importantly, customer satisfaction.
It is also almost impossible for companies that receive plenty of online reviews to analyze them manually for insights. For example, a bank may want to analyze branch-wise customer feedback. teX.ai TM can help extract and analyze reviews of one or more specific branches to understand customer sentiment.
Human resource professionals can also benefit from teX.ai TM’s entity extraction to review resume and CVs, which normally contain information organized and formatted differently. Our entity extraction solution helps recruiters extract relevant information about candidates, from personal information to data pertaining to their work experience, certifications and more.