Microsoft Technology Licensing, LLC

Eric K Ringger

Michael Gamon

Simon H Corston-Oliver

Robert C Moore

Anthony Aue

Richard G Campbell

Lucian Galescu

John R Douceur

Ross Garrett Cutler

Kentaro Toyama

Robert George Atkinson

Daniel Chaim Robbins

Matthew Uyttendaele

Malcolm Erik Pearson

Christopher Brian Quirk

Arul A Menezes

Nuria M Oliver

David Richard Reed

Simon H Corston

Ramaswamy Hariharan

Martine Smets

Zhu Zhang

A location history is a collection of locations over time for an object. A stay is a single instance of an object spending some time in one place, and a destination is any place where one or more objects have experienced a stay. Location histories are parsed using stays and destinations. In a described implementation, each location of a location history is recorded as a spatial position and a corresponding time at which the spatial position is acquired. Stays are extracted from a location history by analyzing locations thereof with regard to a temporal threshold and a spatial threshold. Specifically, two or more locations are considered a stay if they exceed a minimum stay duration and are within a maximum roaming distance. Each stay includes a location, a starting time, and an ending time. Destinations are produced from the extracted stays using a clustering operation and a predetermined scaling factor.

Parsing location histories

The present invention provides a system for identifying, extracting, clustering and analyzing sentiment-bearing text. In one embodiment, the invention implements a pipeline capable of accessing raw text and presenting it in a highly usable and intuitive way.

Method and apparatus for processing sentiment-bearing text

System for processing sentiment-bearing text

A method or module for creating an Language Neutral Syntax (LNS) representation of a sentence from a language particular syntax representation such as found in the Penn Treebank for use by different applications. The method or module includes a node generator configured to create hierarchical and dependent nodes using phrasal and constituent nodes of the language particular syntax. A node dependency generator is configured to create an unordered hierarchical dependency structure for the hierarchical and dependent nodes using a semantic relation between the hierarchical and dependent nodes derived from the language particular syntax.

Module for creating a language neutral syntax representation using a language particular syntax tree

A method of decoding an input semantic structure to generate an output semantic structure. A set of transfer mappings are provided. A score is calculated for at least one transfer mapping in the set of transfer mappings using a statistical model. At least one transfer mapping is selected based on the score and used to construct the output semantic structure.

Statistical language model for logical form using transfer mappings

The present invention is a sentence realization system that processes an abstract linguistic representation (ALR) of a sentence into a structure that can be fully realizable. The system includes a tree conversion component that receives the ALR and generates a basic syntax tree from the ALR. A global movement component then receives the basic syntax tree and hierarchically orders child nodes in that syntax tree relative to ancestor nodes. An intra-constituent ordering component then establishes a linear order among the nodes such that the syntax tree is fully ordered. A surface cleanup component receives the fully ordered tree and performs a number of realization operations to generate surface realizations for constituents that are still represented in an abstract way in the fully ordered syntax tree.

Sentence realization model for a natural language generation system

The present invention relates to systems and methods that determine intent for received data (e.g., email, voice, graphics . . . ) and respond to the data based on the intent. The systems and methods employ various combinations of features based on shallow and deep linguistic analysis (e.g., semantic and syntactic) to yield very high accuracy. The systems and methods analyze and categorize received data to locate data that can include intent. This data can be further refined by extracting features related to the intent. The features can be utilized by a classifier to determine the intent. If the intent warrants a response, the data are further scrutinized and reformulated to generate a description that is indicative of the intent. The reformulation can include representing the features in a logical form, transforming the form and generating a description of the intent that can be presented to a user visually and/or audibly.

Systems and methods that determine intent of data and respond to the data based on the intent

The present invention is a tree ordering component within a sentence realization system which receives an unordered syntax tree and generates a ranked list of alternative ordered syntax trees from the unordered syntax tree. The present invention also includes statistical models of constituent structure employed by the tree ordering component in scoring the alternative ordered trees.

Linguistically informed statistical models of constituent structure for ordering in sentence realization for a natural language generation system

A language model comprising a plurality of augmented-word n-grams and probabilities corresponding to such n-grams. Each n-gram is comprised of a sequence of augmented words. Each augmented word is comprised of the orthographic representation of the word together with a tag representing lexical information regarding the word, such as syntactic or semantic information. Also disclosed are a method of building such a language model, a method of automatically recognizing speech using the language model and a speech recognition system that employs the language model.

Augmented-word language model

Growing community of inventors

Issaquah, WA, United States of America

Eric K Ringger