Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a search engine to provide an entity some indication of topics in which a user may have an interest. The methods, systems, and apparatus include actions of receiving information at a search engine from a classifier indicating that a user is likely interested in a set of topics based on information about the user provided by the search engine to the classifier. Additional actions may include selecting a subset of the topics and generating a search results page that includes one or more references for one or more resources that are responsive to a search query. At least one reference of the one or more references may include information based on the received information that indicates that the user is likely interested in the subset of topics.
Abstract:
Systems and methods are disclosed for targeting effective contributors and identifying high quality contributions. For example, a method may include displaying an advertisement to a potential contributor via an advertising platform, receiving an indication that the potential contributor responded to the advertisement, generating a crowdsourcing exercise that is presented to the contributor, receiving a response (a conversion event) from the contributor to the crowdsourcing exercise, and notifying the advertising platform about the conversion event. As another example, a method may include determining a concept space for a new contribution, obtaining previously correct and incorrect contributions of the contributor in the concept space, and determining an expertise confidence score for the new contribution based on a comparison of the new contribution with the previously correct and incorrect contributions. The method may include automatically approving the new contribution for the crowdsourced repository based on the expertise confidence score.
Abstract:
Methods, systems, and apparatus for obtaining a resource, identifying a first portion of text of the resource that is characterized as a question, and a second part of text of the resource that is characterized as an answer to the question, identifying an entity that is referenced by one or more terms of the text that is characterized as the question, a relationship type that is referenced by one or more other terms of the text that is characterized as the question, and an entity that is referenced by the text that is characterized as the answer to the question, and adjusting a score for a relationship of the relationship type for the entity that is referenced by the one or more terms of the text that is characterized as the question and the entity that is referenced by the text that is characterized as the answer to the question.
Abstract:
Methods and systems are provided for a question answering. In some implementations, a data element to be updated is identified in a knowledge graph and a query is generated based at least in part on the data element. The query is provided to a query processing engine. Information is received from the query processing engine in response to the query. The knowledge graph is updated based at least in part on the received information.
Abstract:
Systems and methods provide distantly supervised wrapper induction for semi-structured documents, including automatically generating and annotating training documents for the wrapper. Training of the wrapper may occur in two phases using the training documents. An example method includes identifying a training set of semi-structured web pages having a subject entity that exists in a knowledge base and, for each training page, identifying target objects, identifying predicates in the knowledge base that connect the subject entity to a target objects identified in the training page, and annotating the training page. Annotating a training page includes generating a feature set for a mention of the target object, generating predicate-target object pairs for the mention, and labeling each predicate-target object pair with a corresponding example type and weight. The annotated training pages are used to train the wrapper to extract new subject entities and new facts from the set of semi-structured web pages.
Abstract:
Methods and systems are provided for a question answering. In some implementations, a data element to be updated is identified in a knowledge graph and a query is generated based at least in part on the data element. The query is provided to a query processing engine. Information is received from the query processing engine in response to the query. The knowledge graph is updated based at least in part on the received information.
Abstract:
The present disclosure provides systems and methods that use machine learning to improve whole-structure relevance of hierarchical informational displays. In particular, the present disclosure provides systems and methods that employ a supervised, discriminative machine learning approach to jointly optimize the ranking of items and their display attributes. One example system includes a machine-learned display selection model that has been trained to jointly select a plurality of items and one or more attributes for each item for inclusion in an informational display. For example, the machine-learned display selection model can optimize a nested submodular objective function to jointly select the items and attributes.
Abstract:
Methods, systems, and apparatus for obtaining a resource, identifying a first portion of text of the resource that is characterized as a question, and a second part of text of the resource that is characterized as an answer to the question, identifying an entity that is referenced by one or more terms of the text that is characterized as the question, a relationship type that is referenced by one or more other terms of the text that is characterized as the question, and an entity that is referenced by the text that is characterized as the answer to the question, and adjusting a score for a relationship of the relationship type for the entity that is referenced by the one or more terms of the text that is characterized as the question and the entity that is referenced by the text that is characterized as the answer to the question.
Abstract:
Location history data can be used to identify attributes for a known geographic location of a business listing record for a business. As an example, location history data is received from each of a plurality of client computing devices. For each device, the location history data identifies an arrival and departure times for the known geographic location as well as prior and next visit information. The location history data is used as input into a classifier in order to identify a set of attributes for the geographic location and corresponding likelihood values for each of the attributes of the set of attributes. A subset of the set of attributes is determined such that the attributes describe aspects of the known geographic location based on the likelihood values. This subset is provided to a client computing device in response to a request for information about the geographic location.