Abstract: An automated technique for tagging documents includes using a semantic tagger to generate an annotation that associates a standard tag with a first text fragment of the user-defined document, wherein the tagger is trained on a standard document annotated with a standard tag, associating the first user-defined tag with a second text fragment of the user-defined document in response to the second text fragment matching a value associated with the first user-defined tag, and establishing a mapping between the standard tag and the first user-defined tag in response to existence of a requisite correlation between the standard tag and the user-defined tag. The technique may further include selecting from the user-defined document a tagged text fragment that is associated with a second user-defined tag, and providing the tagged text fragment and a standard tag associated by the mapping with the second user-defined tag to the tagger as additional training input.

Abstract: Systems and methods for building a prediction model to predict a degree of relevance between digital ads and a search query or webpage content are disclosed. Generally, an indication of relevance is received between a plurality of digital ads and one of a webpage content or a search query. A set of features is extracted from the plurality of digital ads and one of the webpage content or the search query. A prediction model is then built to predict a degree of relevance between the set of candidate digital ads and one of a second webpage content or a second search query, where the prediction model is built based at least one the received indication of relevance and the extracted set of features.

Abstract: Systems and methods for predicting a degree of relevance between a set of candidate digital ads and a search query are disclosed. Generally, an ad provider receives a digital ad request associated with a search query. The ad provider identifies a set of candidate digital ads that may be served in response to the digital ad request. A relevance module extracts a set of features from the set of candidate digital ads and the search query associated with the digital ad request, and determines a degree of relevance between the set of candidate digital ads and the search query based on a prediction model and the extracted set of features. If the relevance module determines the set of candidate digital ads is relevant to the search query, the ad provider may serve one or more digital ads from the set of candidate digital ads in response to the received digital ad request.

Abstract: Systems and methods for predicting a degree of relevance between a set of candidate digital ads and webpage content are disclosed. Generally, an ad provider receives a digital ad request associated with webpage content. The ad provider identifies a set of candidate digital ads that may be served in response to the digital ad request. A relevance module extracts a set of features from the set of candidate digital ads and the webpage content, and determines a degree of relevance between the set of candidate digital ads and the webpage content based on a prediction model and the extracted set of features. If the relevance module determines the set of candidate digital ads is relevant to the webpage content, the ad provider may serve one or more digital ads from the set of candidate digital ads in response to the received digital ad request.

Abstract: A system for optimizing machine-learned ranking functions based on click data. The system determines the weighting for each feature of a plurality of features according to a learning model based on the click data. The system selects an element from a plurality of elements for display on a web page based on the weighting of each feature of the plurality of features. The system may rank the items to form a list on the web page based on the weighted features in order of inferred relevance according to the online learning model.

Abstract: A system for and method for ranking results. The system includes a server configured to receive a query and an advertisement engine configured to receive the query from the server. The advertisement engine ranks advertisements based on various features, including at least one word overlap feature and a correlation feature.

Abstract: An improved system and method for detecting the sensitivity of web page content for serving advertisements in online advertising is provided. A web page sensitivity classifier may be provided for identifying the sensitivity of the content of a web page to an advertisement. The web page sensitivity classifier may use the features of a web page and the features of each advertisement in a list of candidate advertisements to identify advertisements that do not match the sensitivity of the content of the web page. Any advertisements that do not match the sensitivity of the content of the web page may be removed form the list of candidate advertisements. Web page placements may be allocated for advertisements from the list of candidate advertisements that match the sensitivity of the content of the web page, and the advertisements may be served for display.

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: 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: An improved system and method for revising natural language parse trees is provided. A revision dependency parser may learn a set of transformation rules that may be applied to dependency parse trees generated by a base parser for revising the dependency parse trees. A corpus of natural language sentences and a set of correct dependency parse trees may be used to train a revision dependency parser to correct dependency parse trees generated by the base parser. A revision engine may compare the dependency parse trees produced by the base parser with the correct ones present in the training data to produce an observation-rule pair for each dependency. A rule may specify a transformation on the predicted dependency parse tree generated by the base parser to replace an incorrect dependency with a corrected dependency or may change the type of dependency expressed for the grammatical function of the dependent word.