Microsoft Technology Licensing, LLC

Disney Enterprises, Inc.

David Parkinson

Michael V Calcagno

Ravi Chandru Shahani

Domenic J Cipollone

Su Chin Chang

Mari J B Olsen

David Neal Weise

Sonja Sabina Knoll

Guy A Molinari

Sravana Kumar Karnati

Patrick Cook

There is provided a system and method for website content identification in a content management system. The method comprises receiving a first plurality of content identifications, wherein each of the first plurality of content identifications corresponds to each of a first plurality of contents and each of a first plurality of content paths in a content management system, determining a second plurality of content identifications, wherein each of the second plurality of content identifications corresponds to each of a second plurality of contents and each of a second plurality of content paths in the content management system, and displaying the first plurality of content identifications and the second plurality of content identification with corresponding webpage elements on a webpage. The method may further comprise receiving a selection of one of the content identifications and retrieving one of the contents or displaying one of the content paths based on the selection.

Website content identification in a content management system

A system and method configured to dynamically tune the size of a cache stored in a shared memory minimizes the amount of the shared memory consumed by the cache while achieving a desired cache hit ratio. A maximum size of the cache is computed based on a slope, the current cache size, a target hit ratio, and a current hit ratio. The maximum size is then used to dynamically adjust the size of the cache, decreasing or increasing the size based on the computed maximum size.

Dynamically tuning the size of a cache stored in a shared memory

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

A method is provided for forming a reconstructed parse structure for an initial parse structure. Under the method, at least one rule used to form the initial parse structure and at least one additional rule are executed to form the reconstructed parse structure. In many embodiments, the reconstructed parse structure differs from the initial parse structure in that a node is located in a different position in the reconstructed parse structure than in the initial parse structure.

Method and apparatus for reattaching nodes in a parse structure

The present invention uses a machine-learned statistical model to map between unstructured data and structured data. By using machine learning techniques, the present parsing engine can be very quickly and easily trained for new and different locations or domains and can also accommodate for inputs which are unseen in the training data.

System and method for parsing unstructured data into structured data

A method is provided for parsing a sentence to form representations of syntactic structures in the sentence. The method includes forming a first representation of syntactic structures in the sentence. This first representation is then modified by modifying the syntactic structures to form at least two modified syntactic representations. At least two modified representations are then identified as possible representations of syntactic structures in the sentence.

The present invention discloses a method and apparatus for improving the performance of a natural language parser. During training, natural language inputs are processed such that statistical information is extracted, concerning the frequency of attachment of certain phrase types of interest to certain other host phrase types. In one embodiment, only unambiguous attachments are examined during training. During runtime this statistical information is used in cases where the attachment of some phrase type of interest is ambiguous. In such cases, the statistical model is used to evaluate the relative likelihood of the alternative attachments resulting in a good interpretation. A syntactic representation can be produced for each alternative attachment that the statistical model deems likely to result in a good interpretation.

Method and apparatus for training and deployment of a statistical model of syntactic attachment likelihood

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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Seattle, WA, United States of America

David Parkinson