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 GPT-3.

The following topics describe natural language methods:

Method	Example use case	Notes
List	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.
Question method	"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.

Notes

For layout-based extraction methods, see methods.