Use natural-language SenseML methods to extract free text from unstructured documents, or as low-code alternatives to layout based methods. For example, extract information from legal paragraphs in contracts and leases, or results from research papers.
SenseML natural-language methods are powered by machine learning and natural-language processing models, for example by the large-language model (LLM) GPT-4.
The following topics describe how to author natural-language methods using SenseML.
|Example use case
|"For each vehicle in an auto insurance declaration, extract the VIN, model, and year."
|Extracts a list of data out of a document, where you don't know how the data are represented.
|NLP Table method
|"For each transaction in a bank statement table, extract the date and amount."
|Extracts a list of data out of a document, where you know they're in a table.
|"When does the policy period end?"
"What are the last 4 numbers of the account?"
|Extracts a single fact or data point.
|Summarizer computed field method + Topic method
|"list the rents, how often the rent must be paid, and when the rent is due"
|More configurable alternative to the List method.
For information about authoring natural-language methods using a visual tool instead of JSON, see Prompt tips.
For layout-based extraction methods, see methods.
Updated 3 days ago