A natural language query tool comprising cascaded conditional random fields (CRFs) (e.g., a linear-chain CRF and a skip-chain CRF applied sequentially) processes natural language input to produce output that can be used in database searches. For example, cascaded CRFs extract entities from natural language input that correspond to column names or column values in a database, and identify relationships between the extracted entities. A search engine can execute queries based on output from the cascaded CRFs over an inverted index of a database, which can be based on one or more materialized views of the database. Results can be sorted (e.g., according to relevance scores) and presented in a user interface.