Microsoft Technology Licensing, LLC

Other

Microsoft Corporaiton

Microsoft Corporation

Ye-Yi Wang

Alejandro Acero

Yun-Cheng Ju

Dong Yu

Xuedong D Huang

Milind V Mahajan

Jianfeng Gao

Xiaolong Mou

Li Deng

Xiaodong He

Xiao Li

Chenlei Guo

Michael Gamon

Saliha Azzam

Nicholas Caldwell

Xiaolong Li

Leon Wong

Divya Jetley

Yizheng Cai

Byungki Byun

Dilek Zeynep Hakkani-Tur

Gokhan Tur

Xinying Song

Brian D Remick

Yelong Shen

Edward Thiele

Mohammed Aatif Ali

Marcus Gois

Stephen Friesen

John Sie Yuen Lee

Shipeng Li

Zheng Chen

Robert L Rounthwaite

Larry Paul Heck

Ciprian Ioan Chelba

Vipindeep Vangala

Xiaodong Liu

Geoffrey G Zweig

Jian-Tao Sun

Omar Zia Khan

Li Jiang

Nitin Agrawal

Shobana Balakrishnan

Jianshu Chen

Xiaochuan Ni

Zhimin Zhang

Minwoo Jeong

Pankaj Khanzode

Brendan John Frey

Leonard Alan Collins

Ravi Kumar Bikkula

Scott Kirk Imig

Asli Celikyilmaz

Alan J Packer

Yun-Nung Chen

Alex Acero

Bin Zhang

Diego Perez Del Carpio

Mariana Stepp

Kevin Duh

Yang Zou

Jingjing Liu

Shihao Ji

Pavel Metrikov

Raphael Dominik Hoffmann

Craig M Fisher

Sibel Yaman

Mark Cecys

A processing unit can train a model as a joint multi-domain recurrent neural network (JRNN), such as a bi-directional recurrent neural network (bRNN) and/or a recurrent neural network with long-short term memory (RNN-LSTM) for spoken language understanding (SLU). The processing unit can use the trained model to, e.g., jointly model slot filling, intent determination, and domain classification. The joint multi-domain model described herein can estimate a complete semantic frame per query, and the joint multi-domain model enables multi-task deep learning leveraging the data from multiple domains. The joint multi-domain recurrent neural network (JRNN) can leverage semantic intents (such as, finding or identifying, e.g., a domain specific goal) and slots (such as, dates, times, locations, subjects, etc.) across multiple domains.

Multi-domain joint semantic frame parsing

Automatically detected and identified tasks and calendar items from electronic communications may be populated into one or more tasks applications and calendaring applications. Text content retrieved from one or more electronic communications may be extracted and parsed for determining whether keywords or terms contained in the parsed text may lead to a classification of the text content or part of the text content as a task. Identified tasks may be automatically populated into a tasks application. Similarly, text content from such sources may be parsed for keywords and terms that may be identified as indicating calendar items, for example, meeting requests. Identified calendar items may be automatically populated into a calendar application as a calendar entry.

Automatic task extraction and calendar entry

Systems and methods are disclosed to provide optimized scheduling of calendar events based on flexibility scores of calendar events. A flexibility score may be representative of a probability or likelihood that a calendar event can or will be rescheduled in response to a conflicting calendar event. Flexibility scores of calendar events may be calculated based on one or more factors, which may be weighted, using one or more machine-learning models. Factors may include: event densities of invitees' calendars, organizational rankings of respective invitees, the remaining time before an event start time, an urgency of respective calendar events, etc. In this way, if open time slots are not available for all invitees to a proposed calendar request, an event organizer may identify time slots occupied by existing calendar events with the highest likelihood of being rescheduled in view of the proposed calendar event, thereby facilitating scheduling of the proposed calendar event.

Optimized scheduling of calendar events

A computer-implemented technique is described herein for creating a relational data structure by extracting user data items from a collection of one or more applications sources. These data items evince interests exhibited by the users, and may include messages, documents, tasks, meetings, etc. The technique also collects knowledge data items from one or more knowledge sources. In one implementation, these data items may include terms used to describe skills possessed by the users. The technique constructs the data structure by providing objects associated with respective data items, and links between respective pairs of objects. In its real-time phase of operation, the technique allows a user to interrogate the relational data structure, e.g., to identify skills possessed by a particular user, to find users associated with a specified skill, etc.

Querying a relational knowledgebase that provides data extracted from plural sources

Intent determination as a service (IaaS) is disclosed. A third party application may be provided access to an IaaS service. The third party application and the IaaS system may exchange or be provided registration data and information that allow configuration of data and interfaces used in provision of IaaS to the third party application. A query received as input at the third party application may be sent to the IaaS system and the intent of a query may be determined and indicated in a query response sent back to the third party application. A third party application may also interface with a device client application integrated into the operating system of a device as part of accessing an IaaS system. Use of IaaS for queries associated with or relevant to third party applications may extend the capabilities of the third party applications and device client applications.

Extension of third party application functionality for intent determination

Techniques for providing a people recommendation system for predicting and recommending relevant people (or other entities) to include in a conversation based on contextual indicators. In an exemplary embodiment, email recipient recommendations may be suggested based on contextual signals, e.g., project names, body text, existing recipients, current date and time, etc. In an aspect, a plurality of properties including ranked key phrases are associated with profiles corresponding to personal entities. Aggregated profiles are analyzed using first- and second-layer processing techniques. The recommendations may be provided to the user reactively, e.g., in response to a specific query by the user to the people recommendation system, or proactively, e.g., based on the context of what the user is currently working on, in the absence of a specific query by the user.

Contextual people recommendations

Structured web pages are accessed and parsed to obtain implicit annotation for natural language understanding tasks. Search queries that hit these structured web pages are automatically mined for information that is used to semantically annotate the queries. The automatically annotated queries may be used for automatically building statistical unsupervised slot filling models without using a semantic annotation guideline. For example, tags that are located on a structured web page that are associated with the search query may be used to annotate the query. The mined search queries may be filtered to create a set of queries that is in a form of a natural language query and/or remove queries that are difficult to parse. A natural language model may be trained using the resulting mined queries. Some queries may be set aside for testing and the model may be adapted using in-domain sentences that are not annotated. The models may be tested using these implicitly annotated natural-language-like queries in an unsupervised fashion.

Exploiting structured content for unsupervised natural language semantic parsing

A system may comprise one or more processors and memory storing instructions that, when executed by one or more processors, configure one or more processors to perform a number of operations or tasks, such as receiving a query or a document, and mapping the query or the document into a lower dimensional representation by performing at least one operational layer that shares at least two disparate tasks.

Representation learning using multi-task deep neural networks

Techniques for providing a people recommendation system for predicting and recommending relevant people (or other entities) to include in a conversation. In an exemplary embodiment, a plurality of conversation boxes associated with communications between a user and target recipients, or between other users and recipients, are collected and stored as user history. During a training phase, the user history is used to train encoder and decoder blocks in a de-noising auto-encoder model. During a prediction phase, the trained encoder and decoder are used to predict one or more recipients for a current conversation box composed by the user, based on contextual and other signals extracted from the current conversation box. The predicted recipients are ranked using a scoring function, and the top-ranked individuals or entities may be recommended to the user.

Relevance group suggestions

One or more techniques and/or systems are disclosed for creating an expanded or improved lexicon for use in search-based semantic tagging. A set of first documents can be identified using a set of first lexicon elements as queries, and one or more first document patterns can be extracted from the set of first documents. The document patterns can be used to find one or more second documents in a query log that comprise the document patterns, which are associated with query terms used to return the second documents. The query terms for the second documents can be extracted and used to expand the lexicon. Elements within the lexicon may be weighted based upon relevance to different query domains, for example.

Search lexicon expansion

Methods, systems, and computer-readable media for query simplification are provided. A search engine executed by a server receives a query. In response, the search engine determines whether the query is a long or hard query. For long or hard queries, the search engine drops one or more terms based on search engine logs. The search engine may utilize statistical models like machine translation, condition random fields, or max entropy, to identify the terms that should be dropped. The search engine obtains search results for the simplified query and transmits the results to a user that provided the query.

Query simplification

A search engine is described herein for providing search results based on a context in which a query has been submitted, as expressed by context information. The search engine operates by ranking a plurality of documents based on a consideration of the query, and based, in part, on a context concept vector and a plurality of document concept vectors, both generated using a deep learning model (such as a deep neural network). The context concept vector is formed by a projection of the context information into a semantic space using the deep learning model. Each document concept vector is formed by a projection of document information, associated with a particular document, into the same semantic space using the deep learning model. The ranking operates by favoring documents that are relevant to the context within the semantic space, and disfavoring documents that are not relevant to the context.

Context-sensitive search using a deep learning model

A user's search experience may be enhanced by providing additional content based upon an understanding of the user's intent. Query tagging, the assigning of semantic labels to terms within a query, is one technique that may be utilized to determine the context of a user's search query. Accordingly, as provided herein, a query tagging model may be updated using one or more stratified lexicons. A list data structure (e.g., lists of phrases obtained from web pages) and seed distribution data (e.g., pre-labeled probability data) may be used by a graph learning technique to obtain an expanded set of phrases and their respective probabilities of corresponding with particular lexicons (e.g., semantic class lexicons). The expanded set of phrases may be used to group phrases into stratified lexicons. The stratified lexicons may be used as features for updating and/or executing the query tagging model.

Acquisition of semantic class lexicons for query tagging

Embodiments of the present invention relate to systems, methods, and computer-storage media for a method of contextually analyzing terms within a search query. In one embodiment, a received search query is classified into a domain category. Additionally, information is assigned to a schema associated with the domain by analyzing the search query. Further, at least one search result that helps a user complete a task within the domain is provided based on the information in the schema.

Dialog-enhanced contextual search query analysis

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Redmond, WA, United States of America

Ye-Yi Wang