Natural language methods
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.
Updated about 10 hours ago