1. A method of preparing a learning pattern for extracting information from text, said method comprising: receiving an input sample of text as an input into a computer tool executed by a processor on a computer; receiving inputs from a user to name entities within said sample of text; parsing said input sample of text to form a parse tree, using a processor on a computer executing a parser that respects named entities of a Named Entity (NE) Annotator, meaning that the parser treats a named entity as a single token; presenting said parse tree to a user; and receiving user inputs to: specify relation arguments and names of components of said parse tree; define a machine-labeled learning pattern from said parse tree and its associated user inputs, said machine-labeled learning pattern comprising a precedence inclusion pattern wherein elements in said learning pattern are defined in a precedence relation and in an inclusion relation (PI pattern), based on said user's inputs; and store said machine-labeled learning pattern in a memory, said stored learning pattern being available as a query for searching for relation instances in unseen text that matches said PI pattern wherein said user interfaces with said computer tool using: a first menu to permit the user to input a sample text, to select and designate argument names for linguistic elements from a selected sample text, and to construct a relation instance of said linguistic elements; a second menu to permit the user to generate a PI pattern from one or more relation instances generated using said first menu; and a third menu to permit the user to use a PI pattern generated by said second menu to search for undiscovered instances of a relation instance.
1. A method of preparing a learning pattern for extracting information from text, said method comprising: receiving an input sample of text as an input into a computer tool executed by a processor on a computer; receiving inputs from a user to name entities within said sample of text; parsing said input sample of text to form a parse tree, using a processor on a computer executing a parser that respects named entities of a Named Entity (NE) Annotator, meaning that the parser treats a named entity as a single token; presenting said parse tree to a user; and receiving user inputs to: specify relation arguments and names of components of said parse tree; define a machine-labeled learning pattern from said parse tree and its associated user inputs, said machine-labeled learning pattern comprising a precedence inclusion pattern wherein elements in said learning pattern are defined in a precedence relation and in an inclusion relation (PI pattern), based on said user's inputs; and store said machine-labeled learning pattern in a memory, said stored learning pattern being available as a query for searching for relation instances in unseen text that matches said PI pattern wherein said user interfaces with said computer tool using: a first menu to permit the user to input a sample text, to select and designate argument names for linguistic elements from a selected sample text, and to construct a relation instance of said linguistic elements; a second menu to permit the user to generate a PI pattern from one or more relation instances generated using said first menu; and a third menu to permit the user to use a PI pattern generated by said second menu to search for undiscovered instances of a relation instance. 2. The method of claim 1 , further comprising: calculating a generalization of said learning pattern.