About Accelerated Text

Accelerated Text is a “data to text” natural language generation tool which allows you to define data descriptions and then generates multiple versions of those descriptions varying in wording and structure. It can work with much of your data:

• Business metrics
• Customer interaction data
• Product attributes
• Financial metrics

You can generate text for your business reports, your e-commerce platform, or your customer support system.

Philosophy

Natural language generation is a broad domain with applications in chat-bots, story generation, and data description to name a few. Accelerated Text focuses on applying NLG technology to solve your “data to text” needs.

Data descriptions require precision. For example, a text describing weather conditions can not invent things beyond what it was provided: Temperature: -1C, Humidity: 40%, Wind: 10km/h. A generated text can only state those facts. The expression of an individual fact - temperature - could vary. It could result in a "it is cold", or "it is just below freezing", or "-1C" but this fact will be stated because it is in the data. A "data to text" system is also not one to elaborate on a story about the serenity of the freezing lake - again, it was not in the supplied data.

Accelerated Text follows the principle of this strict adherence to the data-bound text generation. Via its user interface it provides instruments to define how the data should be translated into descriptive text. This description - a document plan - is executed by its natural language generation engine to produce texts that vary in structure and wording but are always and only about the data provided.

Accelerated Text on GitHub

Targeted Crawling

Example: crawl 10,000,000 web pages per day and make them available for enterprise search.

Website Parsing

Example: given a list of websites of investment funds, determine the geographic make up of their exposure.

Search and Discovery

Example: index 500,000 quarterly reports, then determine what is important to rank in the top 10 for each query of interest.

Media Parsing

Example: identifying market reactions to fluctuations of commodity prices as manifested in popular media.

Corporate Document Parsing

Example: Retrieve auditor details from a repository of quarterly company reports.

Natural Language Generation

Example: automatically generate monthly employee performance reports for different stakeholders.