Self-learning statistical natural language processing for automatic production of virtual personal assistants
First Claim
1. A computing device for interpreting natural language requests, the computing device comprising:
- a semantic compiler module to generate a semantic model as a function of a corpus of predefined requests, wherein the semantic model includes a plurality of mappings between a natural language request and a semantic representation of the natural language request, wherein the semantic representation identifies a user intent and zero or more slots associated with the user intent; and
a request decoder module to;
(i) receive a representation of speech data indicative of a natural language request;
(ii) convert the representation of speech data to a first lattice of candidate alternatives indicative of the natural language request, wherein to convert the representation of speech data comprises to convert the representation of speech data using a language model generated as a function of a domain-biased web corpus;
(iii) generate, using the semantic model, a lattice of candidate alternatives indicative of the natural language request in response to conversion of the representation of speech data to the first lattice of candidate alternatives, wherein each candidate alternative corresponds to a token of the natural language request;
(iv) assign a composite confidence weight to each candidate alternative as a function of the semantic model;
(v) determine an optimal route through the candidate alternative lattice based on an associated confidence weight; and
(vi) generate a semantic representation of the natural language request as a function of the candidate alternatives of the optimal route.
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Abstract
Technologies for natural language request processing include a computing device having a semantic compiler to generate a semantic model based on a corpus of sample requests. The semantic compiler may generate the semantic model by extracting contextual semantic features or processing ontologies. The computing device generates a semantic representation of a natural language request by generating a lattice of candidate alternative representations, assigning a composite weight to each candidate, and finding the best route through the lattice. The composite weight may include semantic weights, phonetic weights, and/or linguistic weights. The semantic representation identifies a user intent and slots associated with the natural language request. The computing device may perform one or more dialog interactions based on the semantic request, including generating a request for additional information or suggesting additional user intents. The computing device may support automated analysis and tuning to improve request processing. Other embodiments are described and claimed.
49 Citations
20 Claims
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1. A computing device for interpreting natural language requests, the computing device comprising:
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a semantic compiler module to generate a semantic model as a function of a corpus of predefined requests, wherein the semantic model includes a plurality of mappings between a natural language request and a semantic representation of the natural language request, wherein the semantic representation identifies a user intent and zero or more slots associated with the user intent; and a request decoder module to;
(i) receive a representation of speech data indicative of a natural language request;
(ii) convert the representation of speech data to a first lattice of candidate alternatives indicative of the natural language request, wherein to convert the representation of speech data comprises to convert the representation of speech data using a language model generated as a function of a domain-biased web corpus;
(iii) generate, using the semantic model, a lattice of candidate alternatives indicative of the natural language request in response to conversion of the representation of speech data to the first lattice of candidate alternatives, wherein each candidate alternative corresponds to a token of the natural language request;
(iv) assign a composite confidence weight to each candidate alternative as a function of the semantic model;
(v) determine an optimal route through the candidate alternative lattice based on an associated confidence weight; and
(vi) generate a semantic representation of the natural language request as a function of the candidate alternatives of the optimal route. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A method for interpreting natural language requests, the method comprising:
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generating, by a computing device, a semantic model as a function of a corpus of predefined requests, wherein the semantic model includes a plurality of mappings between a natural language request and a semantic representation of the natural language request, wherein the semantic representation identifies a user intent and zero or more slots associated with the user intent; receiving, by the computing device, a representation of speech data indicative of a natural language request; converting, by the computing device, the representation of speech data to a first lattice of candidate alternatives indicative of the natural language request, wherein converting the representation of speech data comprises converting the representation of speech data using a language model generated as a function of a domain-biased web corpus; generating, by the computing device using the semantic model, a lattice of candidate alternatives indicative of the natural language request in response to converting the representation of speech data to the first lattice of candidate alternatives, wherein each candidate alternative corresponds to a token of the natural language request; assigning, by the computing device, a composite confidence weight to each candidate alternative as a function of the semantic model; determining, by the computing device, an optimal route through the candidate alternative lattice based on an associated confidence weight; and generating, by the computing device, a semantic representation of the natural language request as a function of the candidate alternatives of the optimal route. - View Dependent Claims (14, 15, 16, 17, 18)
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19. One or more computer-readable storage media comprising a plurality of instructions that in response to being executed cause a computing device to:
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generate a semantic model as a function of a corpus of predefined requests, wherein the semantic model includes a plurality of mappings between a natural language request and a semantic representation of the natural language request, wherein the semantic representation identifies a user intent and zero or more slots associated with the user intent; receive a representation of speech data indicative of a natural language request; convert the representation of speech data to a first lattice of candidate alternatives indicative of the natural language request, wherein to convert the representation of speech data comprises to convert the representation of speech data using a language model generated as a function of a domain-biased web corpus; generate, using the semantic model, a lattice of candidate alternatives indicative of the natural language request in response to converting the representation of speech data to the first lattice of candidate alternatives, wherein each candidate alternative corresponds to a token of the natural language request; assign a composite confidence weight to each candidate alternative as a function of the semantic model; determine an optimal route through the candidate alternative lattice based on an associated confidence weight; and generate a semantic representation of the natural language request as a function of the candidate alternatives of the optimal route. - View Dependent Claims (20)
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Specification