Microsoft Technology Licensing, LLC

Li Xiong

Junaid Ahmed

Arnold Overwijk

Chenyan Xiong

Chuan Hu

Ziliu Li

Daniel Fernando Campos

Abhishek Kumar Singh

Avnish Kumar Chhabra

Cristina Del Amo Casado

Binit Ranjan Mishra

Muhammad Usman Sharif

Li-Fen Wu

Harsh Gupta

Xiao Liu

Reza Sherafat Kazemzadeh

Yevgeniy Olegovich Razuvayev

Hariharan Jayaraman

A system for extracting key phrase candidates from a corpus of documents, including a processor, a memory, and a program executing on the processor. The system is configured to run a key phrase model to extract one or more key phrase candidates from each document in the corpus and convert each extracted key phrase candidate into a feature vector. The key phrase model also filters the feature vectors to remove duplicates using a classifier that was trained on a set of key phrase pairs with manual labels indicating whether two key phrases are duplicates of each other, to produce remaining key phrase candidates. The system uses the remaining key phrase candidates in a computer-implemented application.

Extracting key phrase candidates from documents and producing topical authority ranking

Document embedding vectors for each document of a corpus may be generated by combining embedding vectors for document subparts, thereby yielding a final embedding vector for the document. A machine learning model is trained using a query corpus and the document corpus, where the model generates a ranking score for a given (query, document) pair. During training, rankings scores are generated using the model, such that the training dataset is further refined using the generated ranking scores. For example, top documents and a negative document may be determined for a given query and subsequently used as training data. Multiple negative documents may therefore be determined for a given query. A negative document for a given query may be determined from the negative documents using noise-contrastive estimation. Such determined negative documents may be evaluated using a loss function during model training, thereby yielding a more robust model for search processing.

Document body vectorization and noise-contrastive training

A system for extracting a key phrase from a document includes a neural key phrase extraction model ('BLING-KPE') having a first layer to extract a word sequence from the document, a second layer to represent each word in the word sequence by ELMo embedding, position embedding, and visual features, and a third layer to concatenate the ELMo embedding, the position embedding, and the visual features to produce hybrid word embeddings. A convolutional transformer models the hybrid word embeddings to n-gram embeddings, and a feedforward layer converts the n-gram embeddings into a probability distribution over a set of n-grams and calculates a key phrase score of each n-gram. The neural key phrase extraction model is trained on annotated data based on a labeled loss function to compute cross entropy loss of the key phrase score of each n-gram as compared with a label from the annotated dataset.

Keyphase extraction beyond language modeling

Systems, methods and computer program products are described herein that can be used to help achieve a safe rollout of software in a production datacenter environment. In accordance with certain embodiments, cloud services requests from certain users of a cloud services system (e.g., users that are authorized to receive cloud services via computing devices running test versions of infrastructure software) are dynamically matched to clusters (groups of commonly-managed computing devices called nodes) that are capable of providing the requested services on nodes running test versions of infrastructure software. Within such clusters, the requested services are provided to the users on a subset of cluster nodes that run a test version of an infrastructure software component, while the remaining cluster nodes are not running the particular test version.

Partitioning and orchestrating infrastructure software deployments for safety and agility across diverse configurations and hardware types

A technique produces a graph data structure based on at least partially unstructured information dispersed over web documents. The technique involves applying a machine-trained model to a set of documents (or, more generally 'document units') to identify topics in the documents. The technique then generates count information by counting the occurrences of the single topics and co-occurrences of parings of topics in the documents. The technique generates conditional probability information based on the count information. An instance of conditional probability information describes a probability that a first topic will occur, given an appearance of a second topic, and a probability that the second topic will occur, given an appearance of the first topic. The technique then formulates the conditional probability information in a graph data structure. The technique also provides an application system that utilizes the graph data structure to provide any kind of computer-implemented service to a user.

Generating a graph data structure that identifies relationships among topics expressed in web documents

Technologies pertaining to ranking webpages in response to receipt of a query are described. A search engine receives a query and identifies webpages that are germane to the query. The search engine ranks the identified webpages to form a ranked list, wherein a first webpage is positioned in the ranked list based upon a static score assigned to the first webpage. The static score is based upon a weight assigned to a hyperlink in a second webpage, wherein the hyperlink points to the first webpage, and further wherein the weight is based upon a value of a feature of the hyperlink, such as a location of the hyperlink on the second webpage when the second webpage is rendered. Further, the second webpage includes several hyperlinks that point to different webpages, wherein each of the several hyperlinks has a different weight assigned thereto.

Ranking computer-implemented search results based upon static scores assigned to webpages

Keyphrase extraction beyond language modeling

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Li Xiong