Microsoft Technology Licensing, LLC

Serge-Eric Tremblay

Omar Rogelio Alonso

Vasileios Kandylas

Anirudh Koul

Siddhartha Sen

Jake M Hofman

Ranjitha Gurunath Kulkarni

Described herein are technologies relating to generating search results responsive to receipt of a query. More specifically, the query is mapped to a topic in response to receipt of a query, and social media accounts that have been labeled as being knowledgeable on the topic are identified. Messages in a message feed of the social media account that are germane to the topic are retrieved, and documents referenced (linked) in the retrieved messages are identified. These documents are positioned in a ranked list based upon the documents being referenced in the messages.

Search engine results for low-frequency queries

Examples are disclosed that relate to grouping query clusters based on intent of a search query. One example provides a system for generating recommended queries related to a search query, the system including a logic subsystem, and memory holding instructions executable by the logic subsystem to receive the search query, execute the search query on an index of pre-computed query clusters to identify a baseline query cluster and one or more neighboring query clusters, form a data structure including queries from within the baseline query cluster and the one or more neighboring query clusters, traverse the data structure to form a hierarchical arrangement of queries, and send the hierarchical arrangement of queries to a computing device from which the search query was received.

Providing query recommendations

Representative embodiments disclose mechanisms to compile documents into a timeline document that tracks the evolution of a topic over time. Social media documents can be used to identify importance or popularity of linked documents (i.e., documents shared by social media in a post, tweet, etc.). A collection of social media documents is analyzed and used to identify a series of n-grams and a ranked list of linked documents. A subset of the ranked list is selected based upon similarity to the series of n-grams. The subset is then summarized and captured, along with underlying supporting data, into an entry of a timeline document. Related entries in different timeline documents can be linked to create a pivot point that allows a user to jump from one timeline to another. Timeline documents can be made available as part of a search performed by a query system.

Compiling documents into a timeline per event

Architecture that enables the identification of entities such as people and content in live broadcasts (e.g., streaming content (e.g., video) of live events) and non-live presentations (e.g., movies), in realtime, using recognition processes. This can be accomplished by extracting live data related to a live event. With respect to people entities, filtering can be performed to identify the named (people) entities from the extracted live data, and trending topics discovered as relate to the named entities, as associated with the live event. Multiple images of the named entities that capture the named entities under different conditions are captured for the named entities. The images are then processed to extract and learn facial features (train one or more models), and facial recognition is then performed on faces in the video using the trained model(s).

Automatic recognition of entities in media-captured events

Aspects of the technology described herein generate a comment-summary interface that can help a user find comments of interest to the user. The comment-summary interface allows a user to access comments according to topics instead of scrolling through all of the comments found in a comment section associated with a primary content, such as a news article on a website, a social post, product review, and such. Categorizing the comments into topics makes more efficient use of computer resources by avoiding the need to display comments that are not of interest to the user. The plurality of comments analyzed by aspects of the technology described herein are unstructured comments. Unstructured comments lack specific subject matter categories designated by a user or preselected by a webpage.

Content categorization

Techniques for designing an awareness engine that organizes and serves popularly discussed and viral online content in response to user search queries. In an aspect, quality online content is identified by analyzing posts by users of a social network over specific time periods. For each item of quality online content identified, a virality score is calculated, and a social signature is constructed. The social signature can be constructed from the content itself, as well as from posts referencing the content. Based on this processing, relevant quality online content having the highest virality scores may be retrieved and served in response to user queries. Further techniques are provided for designing a user interface for the awareness engine.

Awareness engine

Systems and methods for generating a social sketch from social communications are provided. Unlike a typical summary of a subject (or of multiple subjects), a social sketch represents a 'snapshot' summary of the social communications of people during a given period of time. The social sketch is generated by obtaining a corpus of social communications and filtering the social communications according to time. The filtered results are clustered according to the subject matter/topics of the social communications. Selected clusters are identified and the topic, representative high-quality social communications from non-experts and experts are extracted from each of the selected clusters and saved as a social sketch corresponding to the time period.

Social sketches

Non-limiting examples of the present disclosure describe utilization of a social graph, created from evaluation of extracted social media data, to generate temporal snapshots related to social media data. The temporal snapshots enable users to explore different levels of interest related to social media data. In one example, a social media identifier is identified. A social graph may be accessed to evaluate the social media identifier. An exemplary social graph includes clustered relationships developed based on analysis of social media data extracted from at least one social networking service. The social graph may be used to generate a temporal snapshot for the social media identifier. The temporal snapshot may be transmitted to an entry point for output of the temporal snapshot. Other examples are also described including navigation between content of temporal snapshots based on selection of linked data, among other examples.

Generation and management of social graph

One or more systems and/or techniques are provided for automatic closed captioning for media content. In an example, real-time content, occurring within a threshold timespan of a broadcast of media content (e.g., social network posts occurring during and an hour before a live broadcast of an interview), may be accessed. A list of named entities, occurring within the social network data, may be generated (e.g., Interviewer Jon, Interviewee Kathy, Husband Dave, Son Jack, etc.). A ranked list of named entities may be created based upon trending named entities within the list of named entities (e.g., a named entity may be ranked higher based upon a more frequent occurrence within the social network posts). A dynamic grammar (e.g., library, etc.) may be built based upon the ranked list of named entities. Speech recognition may be performed upon the broadcast of media content utilizing the dynamic grammar to create closed caption text.

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Serge-Eric Tremblay