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.

Method	Example use case	Notes
List method	"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.
Query 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 information about authoring natural-language methods using a visual tool instead of JSON, see Sensible Instruct tips.

For layout-based extraction methods, see methods.