Whether data mining or developing a knowledge base for internal use, organizations need to convert unstructured data from text, email, marketing materials, and support communications. The problem is, Big Data is turning the information from millions of new web pages, blogs, tweets, news articles, and comments, into actionable items for business decision making. However, Big Data only works for structured, normalized data. Creating structured, minable, and actionable data at web-scale is well beyond human capacity and requires automation. Plexi NLP delivers the core foundation of that automation.
Examples of the types of extraction to structured data and content tagging that can be performed by Plexi NLP include:
Action Items: Extraction of action items from communications reader or external event handler. Finds appointment requests in emails and discovers completed tasks in support tickets.
People & Places: Identification of people and/or places mentioned in a document enables prioritization of emails, link generation, and categorization.
Keywords: Determination of keywords that define a document's subject matter.
Facts: Detection of key facts, statistics, quantities, and other verifiable data.
Reading Level: Determination of the reading level of a document based on vocabulary, sentence complexity, and other factors.
Date & Time: Extraction of time and date data from a document body or identification about relative temporal information (e.g. "last Tuesday" or "next Friday").