Abstract: Disclosed are methods and apparatus for automatically storing and generating bookmarks. In one embodiment, a search query is received. Information identifying a bookmark representing the search query is automatically stored in association with a set of bookmarks. Search results corresponding to the search query are automatically obtained and provided, where the search results identify one or more documents. When one of the documents is selected, a link to the selected one of the documents is automatically stored in association with the bookmark.

Abstract: In one embodiment, a search query is received. Information identifying a bookmark representing the search query is automatically stored in association with a set of bookmarks. Search results corresponding to the search query are automatically obtained and provided, where the search results identify one or more documents. When one of the documents is selected, a link to the selected one of the documents is automatically stored in association with the bookmark.

Abstract: Techniques are provided for prediction locations of users that submit search queries. A query is received at a search engine. An inverted index is searched to identify one or more geographical locations associated with one or more terms of the received query. The inverted index lists a plurality of query terms and one or more geographical locations associated with each query term. Each geographic location that is associated with a listed query term in the inverted index is a determined location for at least one user previously having submitted the listed term in a search query. A geographical location is predicted for a user that submitted the received query based on the identified one or more geographical locations. In this manner, a location is predicted for the user based on similar queries previously submitted by users.

Abstract: A user submitting a query from a computer at an unknown location is located using a language model. The language model is derived from an aggregation of tweets that were sent from known locations.

Abstract: A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that focuses on capturing subtler linguistic associations between the surrounding content and the content of the advertisement. The system of the present invention implements this goal by means of simple and efficient semantic association measures dealing with lexical collocations such as conventional multi-word expressions like “big brother” or “strong tea”. The semantic association measures are used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the semantic association measures for the advertisements and the surrounding context.

Abstract: Techniques are described to increase the diversity or focus of image search results. A user submits an original query to search for images. A server generates a first results set by executing the original query using metadata associated with each image. The server selects, from the first results set, a specified number of results ranked highest and generates a list of terms from the metadata of each of the results selected. The terms may be only the tags of the results. The server generates an updated query using terms in the list that may be weighted based on the frequency of the term in the list or include only a specified number of the highest occurring terms in the list. The server generates a second results set by executing the updated query using metadata associated with each image. The second results set is then stored and displayed to the user.

Abstract: An ad matching system that includes an interactive client permits a triggering Web page author to provide feedback on a candidate advertisement for the page. Author feedback is used to rank ads for display on the triggering page. Preferably author feedback is also incorporated into ad clustering and/or ad ranking formulae within the system. Also, author credibility is judged based on author feedback and on click through rates of placed ads.

Abstract: A method or system for employing user credibility in electronic media advertising is disclosed.

Abstract: Briefly, one or more embodiments of methods, apparatuses or systems for media or content tagging are described.

Abstract: A method or system for measuring or estimating user credibility is described.

Abstract: A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that takes advantage of machine translation technologies. The system of the present invention implements this goal by means of simple and efficient machine translation features that are extracted from the surrounding context to match with the pool of potential advertisements. Machine translation features used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the machine translation features measures for the advertisements and the surrounding context.

Abstract: Exemplary methods and apparatuses are disclosed that may be used to provide or otherwise support extraction of objects and facets from one or more extraction corpora and ranking of said facets using multiple ranking corpora.

Abstract: A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that focuses on capturing subtler linguistic associations between the surrounding content and the content of the advertisement. The system of the present invention implements this goal by means of simple and efficient semantic association measures dealing with lexical collocations such as conventional multi-word expressions like “big brother” or “strong tea”. The semantic association measures are used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the semantic association measures for the advertisements and the surrounding context.

Abstract: Embodiments of data processing and more specifically of methods, apparatuses and/or systems for use in identifying one or more graphical images and/or ranking or serving graphical images via one or more computing devices are disclosed.

Abstract: Methods, systems and computer program products for associating geographical locations with annotations corresponding to content. In one method, a language model is developed. The language model is developed from the location information and the one or more annotations associated with content uploaded by users. The language model is based on the probabilistic distribution of locations over one or more annotations. Further, when a user provides one or more annotations, the system and the method may use the language model to identify one or more locations associated with the one or more annotations provided by the user. The language model predicts one or more geographical locations based on the probabilistic distribution of locations over the annotations.

Abstract: Methods, systems and computer program products for displaying geographical locations with the one or more annotations. In a particular embodiment, a language model is used to obtain the probability distribution of the locations over one or more annotations. Further, the system and the method utilizes the probability data obtained from the language model to determine a probability score for each location over the one or more annotations. Subsequently, one or more geographical locations are displayed on a world map, based on the probability score of the geographical locations over the one or more annotations. In one embodiment, geographical locations may be highlighted using a color code on a heat map overlaid on the world map. The color code may represent the ranking of the geographical locations based on the calculated probability score for each identified geographical location.

Abstract: A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that takes advantage of machine translation technologies. The system of the present invention implements this goal by means of simple and efficient machine translation features that are extracted from the surrounding context to match with the pool of potential advertisements. Machine translation features used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the machine translation features measures for the advertisements and the surrounding context.

Abstract: Exemplary methods and apparatuses are disclosed that may be used to provide or otherwise support ranking entity relations utilizing the vocabulary of at least one external corpus for use in search engine information management systems.

Abstract: A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that takes advantage of machine translation technologies. The system of the present invention implements this goal by means of simple and efficient machine translation features that are extracted from the surrounding context to match with the pool of potential advertisements. Machine translation features used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the machine translation features measures for the advertisements and the surrounding context.

Abstract: Methods, apparatuses, and systems are provided to determine a response to a user submitted query based, at least in part, on a relationship between and/or among a plurality of terms of the query.