SELF-LEARNING STATISTICAL NATURAL LANGUAGE PROCESSING FOR AUTOMATIC PRODUCTION OF VIRTUAL PERSONAL ASSISTANTS

摘要

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.

主权项

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) 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; (ii) assign a composite confidence weight to each candidate alternative as a function of the semantic model; (iii) determine an optimal route through the candidate alternative lattice based on the associated confidence weight; and (iv) generate a semantic representation of the natural language request as a function of the candidate alternatives of the optimal route; wherein to generate the semantic model comprises to: (i) identify a contextual semantic feature in the corpus using an unsupervised algorithm, wherein the contextual semantic feature comprises a sequence of lexical sets associated with a user intent and zero or more slots associated with the user intent; (ii) determine a first probability of the contextual semantic feature given the user intent; and (iii) determine a normalized probability of the user intent as a function of a rate of occurrence of the contextual semantic feature in the corpus; and wherein to identify the contextual semantic feature using the unsupervised algorithm comprises to: (i) identify predefined named entities and relationships in a first group of predefined sample queries in the corpus; (ii) cluster the predefined sample queries using an unsupervised clustering algorithm to generate a plurality of clusters; and (iii) assign a user intent and slots to each cluster of the plurality of clusters.