Abstract: Systems, methods, and computer storage media are provided for generating rich navigational study aids for electronic books. For a particular section of interest in a document, one or more related sections for providing additional context to the particular section are determined. The related sections are ranked based on a score indicating significance to the particular section. Based on a user's information processing preference, a set of ranked navigational links to each related section is presented to the user for additional context related to the particular section.

Abstract: The presentation style of a video is identified. A set of features that represents the video is computed. A pre-learned video presentation style classifier is then used to weight each of the features in the set of features and determine a presentation style that is predominately employed in the video based on the weighting of the features.

Abstract: Search history data such as browse trails are collected over time. The browse trails, including associated queries and domains, are processed to identify free tokens of the queries that are also modifiers. Attribute value pairs of a structured data source that correspond to the modifiers are determined based on the search history data and a frequency of the attribute value pairs in the structured data source. When a subsequent query is received, modifiers in the query are identified and replaced with the determined combinations of attribute value pairs that correspond to the modifiers in a structured query that is generated from the received query. The structured query is used to identify items and/or services in the structured data source that are responsive to the received query.

Abstract: One or more systems and/or techniques are provided for obtaining content based upon an aspect of an entity. An entity aspect evaluation model is trained based upon burstiness, diversity, and/or uniqueness indicator information for a phrase as relates to an entity, which indicates how likely the phrase is an aspect of the entity (e.g., how likely an “engine fire recall” phrase is as an aspect of a sports car entity). Phrases within social network data (e.g., microblog messages) are evaluated utilizing the trained entity aspect evaluation model to identify whether such phrases are aspects of the entity. Responsive to determining that a phrase is an aspect of the entity, content is obtained (e.g., search results are provided) based upon the aspect. Because the content is obtained based upon a phrase from social network data, the content may pertain to a fresh or trending topic (e.g., due to current social commentary).

Abstract: While a document, such as an e-book, is read by a user on a computing device such as an e-reader, concept phrases are extracted from the document. The extracted concept phrases may be words or phrases that match known concept phrases such as headings. Based on a universal concept phrase graph that includes nodes for each known concept phrase, core concept phrases are determined for the document. These core concept phrases are associated with nodes of the universal concept phrase graph that are located within a predetermined distance of nodes that represent the concept phrases extracted from the document. Each core concept phrase is combined with one or more of the concept phrases to generate multiple queries. These queries are submitted to search engines, and indicators of documents from the corresponding search results are presented to the user with the original document that is being read.

Abstract: Systems, methods, and computer storage media are provided for generating rich navigational study aids for electronic books. For a particular section of interest in a document, one or more related sections for providing additional context to the particular section are determined. The related sections are ranked based on a score indicating significance to the particular section. Based on a user's information processing preference, a set of ranked navigational links to each related section is presented to the user for additional context related to the particular section.

Abstract: Product images are used in conjunction with textual descriptions to improve classifications of product offerings. By combining cues from both text and image descriptions associated with products, implementations enhance both the precision and recall of product description classifications within the context of web-based commerce search. Several implementations are directed to improving those areas where text-only approaches are most unreliable. For example, several implementations use image signals to complement text classifiers and improve overall product classification in situations where brief textual product descriptions use vocabulary that overlaps with multiple diverse categories. Other implementations are directed to using text and images “training sets” to improve automated classifiers including text-only classifiers.

Abstract: A document is received for segmentation. The document includes multiple atomic textual units in a sequence. These units may correspond to sentences, phrases, paragraphs, concept phrases, chapters, etc. A distance function is selected that determines a distance between one set of atomic textual units and another set of atomic textual units. The distance between the sets is large for sets that are dissimilar, and small for sets that are similar. The distance function is applied to the atomic textual units to separate each of the atomic textual units into multiple segments, while maintaining the sequence of the atomic textual units.

Abstract: A document such as a book or textbook includes multiple sections such as chapters. Concept phrases are determined for each of the sections based on the text of each section. A set of content items such as videos is received, and each content item is associated with one or more queries that were submitted by users who were provided the content item in a set of search results. These queries are processed to determine concept phrases that are associated with the content items. The content items and their associated concept phrases are compared with the concept phrases associated with the sections to determine, for some or all of the content items, a minimum subset of the sections whose associated concept phrases cover most of the concept phrases that are associated with the content item. The content items are inserted or linked with the sections in their corresponding minimum subsets.

Abstract: The presentation style of a video is identified. A set of features that represents the video is computed. A pre-learned video presentation style classifier is then used to weight each of the features in the set of features and determine a presentation style that is predominately employed in the video based on the weighting of the features.

Abstract: Text in web pages or other text documents may be classified based on the images or other objects within the webpage. A system for identifying and classifying text related to an object may identify one or more web pages containing the image or similar images, determine topics from the text of the document, and develop a set of training phrases for a classifier. The classifier may be trained and then used to analyze the text in the documents. The training set may include both positive examples and negative examples of text taken from the set of documents. A positive example may include captions or other elements directly associated with the object, while negative examples may include text taken from the documents, but from a large distance from the object. In some cases, the system may iterate on the classification process to refine the results.

Abstract: A framework is provided for composing texts about objects with structured information about these objects, and thus disclosed are methodologies for linking information from at least two data sources—one comprising a plurality of documents comprising text pertaining to at least one object, and one comprising a plurality of structured records comprising at least one characteristic of the at least one object, each characteristic comprising one property name and an associated property value corresponding to the property name for the at least one object—by determining one or more instance-based traits for each object in both data sources and associating at least one record with at least one document that refers to each object, each trait comprising one or more characteristics that identifiably distinguish each object from all other objects.

Abstract: Images stored in an information repository are prepared for browsing. For each image in the repository, text in the repository is mined to extract snippets of text about the image which are semantically relevant to the image, and for each of these snippets of text, keyterms are detected in the snippet of text which represent either concepts that are related to the image or entities that are related to the image, and the snippet of text and keyterms are associated with the image. Each keyterm that is associated with each image in the repository is hyperlinked to each other image in the repository that has this keyterm associated therewith. A graphical user interface allows a user to browse the images in the repository by using their associated snippets of text and hyperlinked keyterms.

Abstract: Product images are used in conjunction with textual descriptions to improve classifications of product offerings. By combining cues from both text and image descriptions associated with products, implementations enhance both the precision and recall of product description classifications within the context of web-based commerce search. Several implementations are directed to improving those areas where text-only approaches are most unreliable. For example, several implementations use image signals to complement text classifiers and improve overall product classification in situations where brief textual product descriptions use vocabulary that overlaps with multiple diverse categories. Other implementations are directed to using text and images “training sets” to improve automated classifiers including text-only classifiers.

Abstract: An image in a web page may be annotated after deriving information about an image when the image may be displayed on multiple web pages. The web pages that show the image may be analyzed in light of each other to determine metadata about the image, then various additional content may be added to the image. The additional content may be hyperlinks to other webpages. The additional content may be displayed as annotations on top of the images and in other manners. Many embodiments may perform searching, analysis, and classification of images prior to the web page being served.

Abstract: Search history data such as browse trails are collected over time. The browse trails, including associated queries and domains, are processed to identify free tokens of the queries that are also modifiers. Attribute value pairs of a structured data source that correspond to the modifiers are determined based on the search history data and a frequency of the attribute value pairs in the structured data source. When a subsequent query is received, modifiers in the query are identified and replaced with the determined combinations of attribute value pairs that correspond to the modifiers in a structured query that is generated from the received query. The structured query is used to identify items and/or services in the structured data source that are responsive to the received query.

Abstract: A method and apparatus for electronically matching an electronic offer to structured data for a product offering is disclosed. The structure data is reviewed and a dictionary of terms for each attribute from the structure data is created. Attributes in unstructured text may be determined. Each pair of the attributes (name and value) from the unstructured data and the structured data are obtained, the attribute pairs of the structured data and the unstructured data and compared and a similarity level is calculated for the matching the attribute pairs. The structured data pair that has the highest similarity score to the unstructured data pair is selected and returned.

Abstract: Described is a technology for obtaining labeled sample data. Labeling guidelines are converted into binary yes/no questions regarding data samples. The questions and data samples are provided to judges who then answer the questions for each sample. The answers are input to a label assignment algorithm that associates a label with each sample based upon the answers. If the guidelines are modified and previous answers to the binary questions are maintained, at least some of the previous answers may be used in re-labeling the samples in view of the modification.

Abstract: Product images are used in conjunction with textual descriptions to improve classifications of product offerings. By combining cues from both text and image descriptions associated with products, implementations enhance both the precision and recall of product description classifications within the context of web-based commerce search. Several implementations are directed to improving those areas where text-only approaches are most unreliable. For example, several implementations use image signals to complement text classifiers and improve overall product classification in situations where brief textual product descriptions use vocabulary that overlaps with multiple diverse categories. Other implementations are directed to using text and images “training sets” to improve automated classifiers including text-only classifiers.

Abstract: Text in web pages or other text documents may be classified based on the images or other objects within the webpage. A system for identifying and classifying text related to an object may identify one or more web pages containing the image or similar images, determine topics from the text of the document, and develop a set of training phrases for a classifier. The classifier may be trained and then used to analyze the text in the documents. The training set may include both positive examples and negative examples of text taken from the set of documents. A positive example may include captions or other elements directly associated with the object, while negative examples may include text taken from the documents, but from a large distance from the object. In some cases, the system may iterate on the classification process to refine the results.