Embodiments of the present disclosure may be utilized to analyze a content item comprising text to identify: a quote, a named entity that is the source of the quote (e.g., a person or organization such as a company), and identification information for the named entity (such as a title of the person giving the quote). Quotes may also be ranked to determine, for example, the best quotes to include in an article.

Systems and methods for content delivery

An entity-based search system is described herein that detects and recognizes entities in Internet-based content and uses this recognition to organize search results. The system associates one or more entity identifiers with a web page and stores this information as metadata of the page in a search engine index. This metadata will enable entity-based queries as well as rich data presentations in a search engine result page (SERP), including grouping results by entities, filtering results by one or more particular entities, or re-ranking search results based on user preference of entities. Thus, the entity-based search system allows users to identify a particular entity the user is interested in finding, and to receive search results directly related to that entity.

Knowledge-based entity detection and disambiguation

Technologies pertaining to the creation of personalized data for an individual, including personalized data based on a social networking participant's social networking data, entity extraction, and automatic collage generation are described herein. In one implementation, the personalized data is used to provide a personalized, social based replacement for a default UI element. In one implementation, a disambiguated named entity is extracted from user generated content in a social networking application, a socially interesting photomontage is automatically generated based on photographs from a social networking application, and the photomontage is rendered with hotspots containing socially relevant annotations.

Social home page

Animated graphical user interface for a display screen or portion thereof

Speech recognition may be improved by generating and using a topic specific language model. A topic specific language model may be created by performing an initial pass on an audio signal using a generic or basis language model. A speech recognition device may then determine topics relating to the audio signal based on the words identified in the initial pass and retrieve a corpus of text relating to those topics. Using the retrieved corpus of text, the speech recognition device may create a topic specific language model. In one example, the speech recognition device may adapt or otherwise modify the generic language model based on the retrieved corpus of text.

Generating Topic-Specific Language Models

Tagging of content items and entities identified therein may include a matching process, a classification process and a disambiguation process. Matching may include the identification of potential matching candidate entities in a content item whereas the classification process may categorize or group identified candidate entities according to known entities to which they are likely a match. In some instances, a candidate entity may be categorized with multiple known entities. Accordingly, a disambiguation process may be used to reduce the potential matches to a single known entity. In one example, the disambiguation process may include ranking potentially matching known entities according to a hierarchy of criteria.

David F. Houghton

Papers

Disambiguation and tagging of entities