Abstract: A method for building and contracting a linguistic dictionary, the linguistic dictionary comprising a list of surface forms and a list of normalized forms, each normalized form being associated with a surface form, the method comprising the steps of: comparing each character of a surface form with each character of the surface form's normalized form; in response to the comparing step, determining an edit operation for each character compared; and generating a transform code from the set of the edit operations in order to transform the surface form to its normalized form.

Abstract: A method for building and contracting a linguistic dictionary, the linguistic dictionary comprising a list of surface forms and a list of normalized forms, each normalized form being associated with a surface form, the method comprising the steps of: comparing each character of a surface form with each character of the surface form's normalized form; in response to the comparing step, determining an edit operation for each character compared; and generating a transform code from the set of the edit operations in order to transform the surface form to its normalized form.

Abstract: A method for building and contracting a linguistic dictionary, the linguistic dictionary comprising a list of surface forms and a list of normalized forms, each normalized form being associated with a surface form, the method comprising the steps of: comparing each character of a surface form with each character of the surface form's normalized form; in response to the comparing step, determining an edit operation for each character compared; and generating a transform code from the set of the edit operations in order to transform the surface form to its normalized form.

Abstract: A method and apparatus for identifying the focus of a document, in a natural language processing application, the natural language processing application comprising a hierarchical concept tree having a plurality of nodes, each node being associated with a term, the method comprising the steps of: mapping an input document to nodes in a concept tree to determine a number of occurrences of a term in the input document which also occur at a node in the concept tree; weighting each node in the concept tree, depending on the determined number of occurrences of the term in the input document and a determined value assigned to each node in the concept tree; traversing the concept tree to identify a heaviest weighted path, in dependence on the weighting of each node in the concept tree; and determining the focus of the input document by identifying a node having the heaviest weight along the most heavily-weighted path.

Abstract: Method and apparatus for finding the focus of a document. A semantic network includes a plurality of nodes, each representing a concept, and links, connecting the nodes, representing relations between the concepts is used. The method including: providing a list of terms in an input document which match concepts represented by the semantic network, and a frequency value for each matched term indicating the number of times the term appears in the document; mapping each matched term to a referent node or to a plurality of possible referent nodes in the semantic network, and assigning weights to nodes.

Abstract: Method and apparatus for finding the focus of a document. A semantic network includes a plurality of nodes, each representing a concept, and links, connecting the nodes, representing relations between the concepts is used. The method including: providing a list of terms in an input document which match concepts represented by the semantic network, and a frequency value for each matched term indicating the number of times the term appears in the document; mapping each matched term to a referent node or to a plurality of possible referent nodes in the semantic network, and assigning weights to nodes.

Abstract: A method and apparatus for identifying the focus of a document, in a natural language processing application, the natural language processing application comprising a hierarchical concept tree having a plurality of nodes, each node being associated with a term, the method comprising the steps of: mapping an input document to nodes in a concept tree to determine a number of occurrences of a term in the input document which also occur at a node in the concept tree; weighting each node in the concept tree, depending on the determined number of occurrences of the term in the input document and a determined value assigned to each node in the concept tree; traversing the concept tree to identify a heaviest weighted path, in dependence on the weighting of each node in the concept tree; and determining the focus of the input document by identifying a node having the heaviest weight along the most heavily-weighted path.

Abstract: A method for building and contracting a linguistic dictionary, the linguistic dictionary comprising a list of surface forms and a list of normalized forms, each normalized form being associated with a surface form, the method comprising the steps of: comparing each character of a surface form with each character of the surface form's normalized form; in response to the comparing step, determining an edit operation for each character compared; and generating a transform code from the set of the edit operations in order to transform the surface form to its normalized form.