The Text Mining and Text Analytics Service is an integral component of our Semantic Tagging Solution, but it can also be part of a customized package to meet your unique needs. By developing a brand new tailored pipeline or fine-tuning an existing one, our experts can optimize your text analytics solutions to achieve the highest quality for your particular task.
Our tagging services analyze the text, extract concepts, identify topics, keywords and important relationships, while taking care of properly disambiguating similarly sounding entities.Our tagging services analyze the text, extract concepts, identify topics, keywords and important relationships, while taking care of properly disambiguating similarly sounding entities.
Make use of context-sensitive analysis and classification for categorizing and organizing your unstructured content.
Identify and extract structured text from documents with the option to add bounding boxes.
This is what puts the "deep" in deep learning. Each layer categorizes some kind of information, refines it, and passes it along to the next. The parallel computing nature of GPUs accelerates this process to enable breakthroughs like facial recognition, real-time voice translation, and self-driving cars.
Text mining, also referred to as text data mining, similar to text analytics, is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources.
Document classification or document categorization is a problem in library science, information science and computer science. The task is to assign a document to one or more classes or categories.
Semantic Normalization, the process of representing information in a consistent and transparent way that enables querying the EHR in different ways and getting back consistent answers, is the very large missing piece.
Information retrieval or identification of a corpus is a preparatory step: collecting or identifying a set of textual materials, on the Web or held in a file system, database, or content corpus manager, for analysis.
Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.
Identification of sets of similar text documents, Document clustering (or text clustering) is the application of cluster analysis to textual documents. It has applications in automatic document organization, topic extraction and fast information retrieval or filtering.
A pattern entity is sort of like a master entity that helps to identify, contextualize, and govern similar types of content. In this example scenario, you've created an NLU intent that's titled Reset Password. In this example procedure, you're creating a pattern entity from a word in an utterance example that you provided in that intent
Pattern recognition is the automated recognition of patterns and regularities in data. It has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning
The ClientoClarify.AI network of partners includes business software providers, niche technology developers, and platform and IT infrastructure vendors.
