US 2015/0032443 A1 · SELF-LEARNING STATISTICAL NATURAL LANGUAGE PROCESSING FOR AUTOMATIC PRODUCTION OF VIRTUAL PERSONAL ASSISTANTS

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.

Claims25

Dependent claims

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:
generate, using the semantic model, a lattice of candidate alternatives indicative of a natural language request, 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 the associated confidence weight;
and
generate a semantic representation of the natural language request as a function of the candidate alternatives of the optimal route.

5 more elements

A method for interpreting natural language requests, the method comprising:

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;
generating, by the computing device using the semantic model, a lattice of candidate alternatives indicative of a natural language request, 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 the 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.

3 more elements

One or more computer-readable storage media comprising a plurality of instructions that in response to being executed cause a computing device 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;
generate, using the semantic model, a lattice of candidate alternatives indicative of a natural language request, 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 the associated confidence weight;
and
generate a semantic representation of the natural language request as a function of the candidate alternatives of the optimal route.

3 more elements

Dependencies are inferred from the claim text.

Self-learning statistical natural language processing for automatic production of virtual personal assistants

Life of this patent

Self-learning statistical natural language processing for automatic production of virtual personal assistants

Life of this patent

Abstract

Claims25