Semantic network methods to disambiguate natural language meaning
First Claim
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1. A computer-implemented method for disambiguating natural language meaning, the computer-implemented method comprising:
- employing a processor to execute computer-readable instructions that, if executed, cause the processor to perform;
receiving a natural language input comprising a plurality of symbols;
storing a plurality of input nodes in a semantic network, wherein at least one of the plurality of input nodes represents at least one of the plurality of symbols, wherein at least some of the input nodes have polysemy, wherein a plurality of candidate meanings for at least one of the input nodes having polysemy are stored respectively as a plurality of candidate nodes in the semantic network, and wherein the semantic network includes a stored natural language context, the stored natural language context including a plurality of context nodes;
identifying a plurality of semantic links, wherein at least one of the plurality of semantic links traverses from at least one of the plurality of candidate nodes to at least one of the plurality of context nodes;
computing at least one contextual distance, the at least one contextual distance corresponding to at least one of the plurality of semantic links;
comparing at least one of the plurality of contextual distances to determine a contextual distance for at least one of the input nodes having polysemy;
selecting at least one of the plurality of candidate nodes for at least one of the input nodes having polysemy, wherein at least one of the selected candidate nodes has an associated contextual distance approximating the contextual distance for the corresponding input node;
outputting a contextual meaning corresponding to the natural language input, wherein the contextual meaning comprises at least one of the candidate meanings corresponding to the selected candidate nodes; and
storing in the semantic network a semantic inheritance link between at least one of the input nodes having polysemy and the corresponding at least one selected candidate node.
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Abstract
A computer implemented data processor system automatically disambiguates a contextual meaning of natural language symbols to enable precise meanings to be stored for later retrieval from a natural language database, so that natural language database design is automatic, to enable flexible and efficient natural language interfaces to computers, household appliances and hand-held devices.
483 Citations
19 Claims
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1. A computer-implemented method for disambiguating natural language meaning, the computer-implemented method comprising:
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employing a processor to execute computer-readable instructions that, if executed, cause the processor to perform; receiving a natural language input comprising a plurality of symbols; storing a plurality of input nodes in a semantic network, wherein at least one of the plurality of input nodes represents at least one of the plurality of symbols, wherein at least some of the input nodes have polysemy, wherein a plurality of candidate meanings for at least one of the input nodes having polysemy are stored respectively as a plurality of candidate nodes in the semantic network, and wherein the semantic network includes a stored natural language context, the stored natural language context including a plurality of context nodes; identifying a plurality of semantic links, wherein at least one of the plurality of semantic links traverses from at least one of the plurality of candidate nodes to at least one of the plurality of context nodes; computing at least one contextual distance, the at least one contextual distance corresponding to at least one of the plurality of semantic links; comparing at least one of the plurality of contextual distances to determine a contextual distance for at least one of the input nodes having polysemy; selecting at least one of the plurality of candidate nodes for at least one of the input nodes having polysemy, wherein at least one of the selected candidate nodes has an associated contextual distance approximating the contextual distance for the corresponding input node; outputting a contextual meaning corresponding to the natural language input, wherein the contextual meaning comprises at least one of the candidate meanings corresponding to the selected candidate nodes; and storing in the semantic network a semantic inheritance link between at least one of the input nodes having polysemy and the corresponding at least one selected candidate node. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A computer-implemented method for disambiguating natural language meaning, the computer-implemented method comprising:
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employing a processor to execute computer-readable instructions that, if executed, cause the processor to perform; receiving an input sequence of symbols, the input sequence of symbols being in a sequence order, the sequence of symbols having a natural language meaning determined from a context node filter and a contextual distance function; storing the sequence of symbols as a new set of semantic network nodes in a semantic network, the new set of semantic network nodes being stored as a linked list having the sequence order, wherein the semantic network has a plurality of semantic network links, wherein the semantic network is stored in a memory of a computer system; storing the natural language meaning as a set of meaning nodes in the semantic network; storing the natural language meaning of the new set of semantic network nodes and of the meaning nodes in the semantic network, such that at least one of the nodes of the new set of semantic network nodes has a semantic network link to at least one node selected from the set of meaning nodes; and retrieving the natural language meaning for the received input sequence of symbols from the semantic network.
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17. A computer-implemented method for disambiguating natural language meaning, the computer-implemented method comprising:
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employing a processor to execute computer-readable instructions that, if executed, cause the processor to perform; receiving an input sequence of symbols, the sequence of symbols being in a sequence order, the sequence of symbols having a natural language meaning determined from a context node filter and a contextual distance function; storing the sequence of symbols as a new set of semantic network nodes in a semantic network, the new set of semantic network nodes being stored as successive input sequences having the sequence order, wherein the a semantic network has a plurality of semantic network links, wherein the semantic network is stored in a memory of a computer system; storing the natural language meaning as a set of meaning nodes in the semantic network; and retrieving the natural language meaning for the received input sequence of symbols from the semantic network. - View Dependent Claims (18)
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19. A computer-implemented method for disambiguating natural language meaning, the computer-implemented method comprising:
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employing a processor to execute computer-readable instructions that, if executed, cause the processor to perform; receiving an input sequence of symbols having a meaning determined from a context node filter and a contextual distance function; storing the sequence of symbols in a semantic network having a plurality of nodes, wherein the sequence of symbols is stored as pairs of subject and predicate nodes linked by verb nodes, wherein the stored pairs of subject and predicate nodes are linked by verb nodes inheriting from a previously stored set of a plurality of meaning nodes, wherein the semantic network is stored in a memory of a computer system; scanning the semantic network for at least one common meaning node in a plurality of meaning nodes corresponding to the stored pairs of subject and predicate nodes linked by verb nodes; creating in the semantic network one or more is a links between at least one pair of subject and predicate nodes of the same input sequence; deleting from the semantic network verb nodes and duplicate pairs of subject and predicate nodes in other stored input sequences from the at least one pair of subject and predicate nodes of the same input sequence; and retrieving the natural language meaning for the received input sequence of symbols.
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Specification