Microsoft Technology Licensing, LLC

Microsoft Corporation

Michael V Calcagno

Su Chin Chang

Ravi Chandru Shahani

David Parkinson

Domenic J Cipollone

Mari J B Olsen

Saliha Azzam

David Neal Weise

Sonja Sabina Knoll

Pär Jonas Barklund

Lei Zhao

Kevin William Humphreys

Allan Folting

Charles David Ellis

Kevin Roland Powell

Nicholas Caldwell

Michael D Plumpe

Jon Hamaker

Evelyne Viegas

Travis Wilson

Rob Chambers

Christopher Dac Le

Robert Stumberger

Kevin R Powell

Sonja S Knoll

David N Weise

The present disclosure describes an exemplary hybrid client/server architecture that may be utilized leverage the unique capabilities of both remote and local services. Data may be processed in parallel by remote and local processes. Results generated during the parallel processing may be exchanged between remote and local services and used to update results generated by the separate services. The hybrid client/server architecture may be utilized to generate enhanced inferences, hybrid subscriptions base upon local and remote subscriptions, and enhance natural language expression evaluation services.

Hybrid client/server architecture for parallel processing

A system and method for normalizing a discourse representation structure (DRS) are provided. The elements of the structure are rewritten and sorted in a way such that structures which may appear different but are nonetheless equivalent can be associated with the same, normalized representation. Various embodiments can also include a data structure for a DRS. The DRS may be represented by an array of boxes, each having a set of elements which in turn has a predefined structure suitable for representing a wide variety of linguistic information.

System for normalizing a discourse representation structure and normalized data structure

A framework for generating a semantic interpretation of natural language input includes an interpreter, a first set of types, and a second set of types. The interpreter is adapted to mediate between a client application and one or more analysis engines to produce interpretations of the natural language input that are valid for the client application. The first set of types is adapted to define interactions between the interpreter and the one or more analysis engines. The second set of types is adapted to define interactions between the interpreter and the client application.

Semantic framework for natural language programming

A software development tool for programming natural language software applications is described. The software development tool includes a programming language and a compiler. The programming language has a set of programming constructs for facilitating natural language programming. The compiler is adapted to take a software program containing instances of the set of programming constructs and to generate a software application.

Semantic programming language

A lexical semantic structure for modeling semantics of a natural language input on a computer is described. A set of lexical semantic categories is selected to model content of the natural language input. A methodology associates content of the natural language input to one or more categories of the set of lexical semantic categories.

Lexical semantic structure

A system for developing natural language enabled software applications is described. Resolvable types define abstract representations of linguistic elements and interrelationships between linguistic elements of a natural language input. Resolution semantics define procedural rules for resolving validity of the resolvable types in a natural language enabled software application.

Resolvable semantic type and resolvable semantic type resolution

The present invention relates to a method of manipulating a software application and processing data stored in a data source. The method includes receiving a natural language input and analyze the natural language input to identify semantic information contained therein. Portions of the natural language input are associated with command objects and entity objects of a schema based on the semantic information and the natural language input. The method also includes rendering data from the data source in a table of columns and rows based on the schema and the associated portions of the natural language input.

Rendering tables with natural language commands

The present invention is a system and method for performing semantic analysis that interprets a linguistic structure output by a natural language linguistic analysis system. The semantic analysis system converts the linguistic output by the natural language linguistic analysis system into a data structure model referred to as a semantic discourse representation structure (SemDRS).

Semantic analysis system for interpreting linguistic structures output by a natural language linguistic analysis system

The present invention is a named entity (NE) interface to a linguistic analysis layer. The interface exposes each input sentence to the NE recognizers of all applications and returns all recognized NEs. Thus, the present invention can accommodate NEs which dynamically change in the applications, because each input string will be handed to the applications for NE recognition. The present invention also includes a data structure which is a normalized form of recognized NEs.

Named entity (NE) interface for multiple client application programs

The present invention is a system and method for normalizing a discourse representation structure (DRS). The elements of the structure are rewritten and sorted in a way such that structures which may appear different but are nonetheless equivalent can be associated with the same, normalized representation. The present invention can also include a data structure for a DRS. The DRS is represented by an array of boxes, each having a set of elements which in turn has a predefined structure suitable for representing a wide variety of linguistic information.

A method of, and system for, generating a sentence from a semantic representation maps the semantic representation to an unordered set of syntactic nodes. Simplified generation grammar rules and statistical goodness measure values from a corresponding analysis grammar are then used to create a tree structure to order the syntactic nodes. The sentence is then generated from the tree structure. The generation grammar is a simplified (context free) version of a corresponding full (context sensitive) analysis grammar. In the generation grammar, conditions on each rule are ignored except those directly related to the semantic representation. The statistical goodness measure values, which are calculated through an analysis training phase in which a corpus of example sentences is processed using the full analysis grammar, are used to guide the generation choice to prefer substructures most commonly found in a particular syntactic/semantic context during analysis.

Statistically driven sentence realizing method and apparatus

A linguistic object model is adapted to model semantic elements of a natural language. The linguistic object model includes a set of abstract representations of semantic elements of an utterance. Each abstract representation in the set corresponds to a natural language type that is independent of any particular language.

Linguistic object model

A method is provided for identifying non-local relationships between licensing elements in a text segment and a word or phrase external to the text segment during a syntactic parse. Under the method, certain syntactic rules for combining words or phrases with text segments indicate that there is a possibility that the word or phrase being combined with the text segment will fill a gap in a relationship within the text segment. Based on this possibility, the text segment is searched to determine if there are any unfilled gaps in the text segment. Under some embodiments, if an unfilled gap is found, the location of the gap and the role the word or phrase plays in the gap are stored in a data structure associated with the syntactic node formed by combining the word or phrase with the text segment.

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Kirkland, WA, United States of America

Michael V Calcagno