Yahoo! Inc.

Microsoft Technology Licensing, LLC

Verizon Media Inc.

Yahoo Holdings, Inc.

Srinivas Vadrevu

Su-Lin Wu

Franco Salvetti

Ben M Shahshahani

Emre Velipasaoglu

Belle L Tseng

Bo Long

Deepayan Chakrabarti

Lei Duan

Swapnil Hajela

Ya Zhang

Fan Li

Xin Li

Qifa Ke

Yi Chang

Zhaohui Zheng

Kunal Punera

Suju Rajan

Gang Luo

Sudarshan Lamkhede

Choon Hui Teo

Qi Liu

Oana Nicolov

Ying Tu

Gordon Guo-Zheng Sun

Krishna Sravanthi Rajanala Sai

Dario Bigongiari

Methods and systems are provided for improving efficiency and precision in factual question answering in a search environment. In aspects, the methods and systems involve offline identification of answers correlated with one or more factual questions that are generated offline for each document within a document collection. Thereafter, upon receiving a factoid query during runtime, candidate documents that are retrieved in response to the factoid query are scanned for a factual question that matches the factoid query. For each factual question that matches the factoid query, a corresponding candidate answer can be identified. Thereafter, each candidate answer can be assigned a score that is a function of the number of candidate documents in which the candidate answer was identified and the relevancy of those candidate documents to the factoid query. The candidate answer having the highest score can be provided to the user in response to the factoid query.

Ranking for efficient factual question answering

News search and browse experience is personalized based on user preferences. User attributes like a geographic location are obtained and news sources preferred by other users with attributes similar to those of a requesting user are identified. News sources that are popular across different user groups are eliminated and relevant news items from the remaining news sources are retrieved and presented to the requesting user.

Personalization of news articles based on news sources

A summary of a document is generated in near real time. In aspects, an indication to summarize the document is received and the document is processed to generate a summary. For instance, processing includes extracting sentences from the document and generating a plurality of candidate passages from the extracted sentences. Features are extracted from each of the plurality of candidate passages and each candidate passage is ranked based at least in part on the extracted features. High-ranking candidate passages are considered likely to be important and/or representative of the document. A summary of the document is generated including one or more of the high-ranking candidate passages. The summary includes portions of the document that are considered important and/or representative of the document, so a user may review the summary in lieu of reading the entire document.

Automatic document summarization using search engine intelligence

Architecture that enables search engines to meet user expectations for search results (e.g., questions-answers) by improving on result consistency. This is attained by declining to answer queries when it is known by the system that the system is unable to answer more or equivalently prominent queries in the same query class in a vast majority of cases. To achieve consistency, queries are categorized into classes and then the queries of a specific class are segmented into clusters. The answer density in each cluster is then computed to determine the consistency of that class of queries. Clusters with a relatively low answer density are then suppressed to improve consistency for the user.

Search engine results system using entity density

Exemplary methods and apparatuses are provided which may be used for classifying and indexing segmented portions of web pages and providing related information for use in information extraction and/or information retrieval systems. In an embodiment, an index of segmented portions may be used by a search engine to respond to a search query. In an embodiment, one or more machine learned models may be used to identify one or more feature properties of a plurality of segmented portions within one or more files, or otherwise inferable from the one or more files. In an embodiment, one or more machine learned models may be used to classify one or more of a plurality of segmented portions as being at least one of a plurality of segment types.

Automatic classification of segmented portions of web pages

Exemplary techniques are provided which may be implemented using various methods, apparatuses, and/or articles of manufacture to provide or otherwise support segment sensitive query matching based on segmented portions of web pages and/or providing related information for use in information extraction and/or information retrieval systems. In certain example implementations techniques may be provided for determining whether a query match exists between a document and obtained query terms based, at least in part, on labeled portion information associated with a plurality of segmented portions of a document.

Segment sensitive query matching

One particular embodiment clusters a plurality of documents using one or more clustering algorithms to obtain one or more first sets of clusters, wherein: each first set of clusters results from clustering the documents using one of the clustering algorithms; and with respect to each first set of clusters, each of the documents belongs to one of the clusters from the first set of clusters; accesses a search query; identifies a search result in response to the search query, wherein the search result comprises two or more of the documents; and clusters the search result to obtain a second set of clusters, wherein each document of the search result belongs to one of the clusters from the second set of clusters.

Clustering of search results

Architecture that addresses at least the problem of ranking the relevant attributes for a given entity within the context of a structured knowledge base (SKB). The architecture utilizes the attribute, entity type statistics, and the taxonomy of the attributes to consistently and efficiently rank attributes for each and every type of entity in the SKB. Using the SKB, intermediate features are computed, including the importance or popularity each entity type for every entity, inverse document frequency (IDF) computation for each attribute on a global basis, IDF computation for entity types, and the popularity of attributes for each entity type. The intermediate features are aggregated to obtain a final feature set, which can be used in combination with human judgments to train a machine learned classifier model to produce and predict a relevance score for a given entity and each of its attributes. The attributes are ranked for each entity using this score.

Ranking relevant attributes of entity in structured knowledge base

Exemplary methods and apparatuses are provided which may be used for classifying and indexing segmented portions of web pages and providing related information for use in information extraction and/or information retrieval systems.

Disclosed are methods and apparatus for clustering news stories, which are to be presented over a computer network. In general, an incremental clustering system is configured to update a current set of news clusters with newly arrived news articles without having to recompute the clusters for the entire corpus, as well as form new clusters for recently generated news topics. In one embodiment, a plurality of news articles are initially obtained via the computer network, and the news articles are clustered into a plurality of initial clusters. For only news articles, including any unclustered news articles, that are less than a predetermined age limit, it is determined in an incremental clustering process whether to form one or more new clusters or assign to the initial clusters. Indications of the initial clusters and the one or more new clusters, if any, are then stored so as to be accessible for sending a portion of the news articles to users in a clustered format based on the initial clusters and the one or more new clusters, if any.

System for incrementally clustering news stories

According to an example embodiment, a method comprises executing instructions by a special purpose computing apparatus to, for labeled source domain data having a plurality of original labels, generate a plurality of first predicted labels for the labeled source domain data using a target function, the target function determined by using a plurality of labels from labeled target domain data. The method further comprises executing instructions by the special purpose computing apparatus to apply a label relation function to the first predicted labels for the source domain data and the original labels for the source domain data to determine a plurality of weighting factors for the labeled source domain data. The method further comprises executing instructions by the special purpose computing apparatus to generate a new target function using the labeled target domain data, the labeled source domain data, and the weighting factors for the labeled source domain data, and evaluate a performance of the new target function to determine if there is a convergence.

System and method for cross domain learning for data augmentation

A system for determining the intent of query that includes a search engine that receives a first search query, a query/click log module configured to store log data associated with the first search query; and a computational module that generates metric values associated with the first search query based on the log data and that determines that the first search query is one of a regional specific query or a global query based on the metric values, where the metric values reflect a likelihood of local intent of the first search query, and where the search engine provides search results selected in part based on the metric values.

Growing community of inventors

Milpitas, CA, United States of America

Srinivas Vadrevu