An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

We present a syntax-based statistical translation model. Our model transforms a source-language parse tree into a target-language string by applying stochastic operations at each node. These operations capture linguistic differences such as word order and case marking. Model parameters are estimated in polynomial time using an EM algorithm. The model produces word alignments that are better than those produced by IBM Model 5.

https://faculty.washington.edu/fxia/courses/LING580MT/yamada_2001.pdf

A Syntax-based Statistical Translation Model

A modular intelligent personal agent system is presented for search, navigation, control, retrieval, analysis, and results reporting on networks and databases. A client-side or server-side software application retrieves and interprets hypertext documents executing a search algorithm, which search results are displayed in alternate three-dimensional and two-dimensional graphical visualization formats. Hypertext documents and associated content media are displayed as symbol or thumbnail web documents as nodes with connector lines representing links between the documents. Nodes and connector lines are color-coded symbol form for the user according to truth of search terms, numeric data tested in hypertext documents, according to domain type, link density, and metric counts. Different symbols represent search and Boolean evaluation status, document type, and thumbnails represent whole or incremental portions of the document page or type documents found. The three-dimensional displayed nodes are visually navigated based on recency, chronology of discovery and metric information values. The result of searches performed by the system can retrieve user selected documents from a network and automatically format results of the search and content retrieval using a plurality of ranking methods. The system provides alerts and content delivery to users using email, instant messaging and audio. Multiple agents can operate to accomplish complex tasks a singular agent cannot. Agents are deployed

Method and apparatus for search, visual navigation, analysis and retrieval of information from networks with remote notification and content delivery

A virtual assistant uses context information to supplement natural language or gestural input from a user. Context helps to clarify the user's intent and to reduce the number of candidate interpretations of the user's input, and reduces the need for the user to provide excessive clarification input. Context can include any available information that is usable by the assistant to supplement explicit user input to constrain an information-processing problem and/or to personalize results. Context can be used to constrain solutions during various phases of processing, including, for example, speech recognition, natural language processing, task flow processing, and dialog generation.

Using context information to facilitate processing of commands in a virtual assistant

Methods and systems for entity recognition and disambiguation using natural language processing techniques are provided. Example embodiments provide an entity recognition and disambiguation system (ERDS) and process that, based upon input of a text segment, automatically determines which entities are being referred to by the text using both natural language processing techniques and analysis of information gleaned from contextual data in the surrounding text. In at least some embodiments, supplemental or related information that can be used to assist in the recognition and/or disambiguation process can be retrieved from knowledge repositories such as an ontology knowledge base. In one embodiment, the ERDS comprises a linguistic analysis engine, a knowledge analysis engine, and a disambiguation engine that cooperate to identify candidate entities from a knowledge repository and determine which of the candidates best matches the one or more detected entities in a text segment using context information.

Nlp-based entity recognition and disambiguation

An extension of an inverse inference search engine is disclosed which provides cross language document retrieval, in which the information matrix used as input to the inverse inference engine is organized into rows of blocks corresponding to languages within a predetermined set of natural languages. The information matrix is further organized into two column-wise partitions. The first partition consists of blocks of entries representing fully translated documents, while the second partition is a matrix of blocks of entries representing documents for which translations are not available in all of the predetermined languages. Further in the second partition, entries in blocks outside the main diagonal of blocks are zero. Another disclosed extension to the inverse inference retrieval document retrieval system supports automatic, knowledge based training. This approach applies the idea of using a training set to the problem of searching databases where information that is diluted or not reliable enough to allow the creation of robust semantic links. To address this situation, the disclosed system loads the left-hand partition of the input matrix for the inverse inference engine with information from reliable sources.

Giovanni B. Marchisio

Papers

Extended functionality for an inverse inference engine based web search