Abstract: A method is disclosed for expansion of rare queries to improve advertisement results, including receiving a query from a user by a search engine; determining that the query does not match an entry in an ad query lookup table coupled with the search engine; retrieving one or more expanded queries located within a query feature index whose features relate to one or more features of the received query, wherein the query feature index includes a plurality of queries expanded based on at least corresponding search results; generating, in real time and by the search engine, an ad query including an expanded version of the received query based on features of the retrieved expanded queries; and selecting one or more advertisements based on the generated ad query, wherein the one or more advertisements are displayed to the user in response to the query received from the user.

Abstract: A system and method for improved search relevance using proximity boosting. A query for a web search is received from a user, via a network, wherein the query comprises a plurality of query tokens. One or more concepts are identified in the query wherein each of concepts comprises at least two query tokens. A relative concept strength is determined for each of the identified concepts. The query is then rewritten for submission to a search engine wherein for each of the one or more concepts, a syntax rule associated with the respective relative concept strength of the concept is applied to the query tokens comprising the concept such that the rewritten query represents the one or more concepts whereby the proximity of the one or more concepts in a search result returned by the search engine to the user in response to the rewritten query is boosted.

Abstract: Disclosed herein are systems and methods for identifying phrases using break points. Break points can be identified using stop words identified in content. Identified phrases can be used to generate a summary of the content.

Abstract: Methods and apparatus are described for identifying implicitly temporally qualified queries, i.e., queries for which a time period is implied but not explicitly stated, and for expanding such queries to include one or more temporal references.

Abstract: The present invention is directed towards systems, methods and computer program products for providing query-based advertising content. According to one embodiment, a method for providing query-based advertising content comprises receiving a web query and generating an ad query associated with the web query, wherein the ad query is generated on the basis of one of a query to advertisement relationship, a query to feature vector relationship, or a rewritten query vector. An advertisement is selected on the basis of the generated ad query.

Abstract: A predictor for determining a degree of relevance between a query rewrite and a search query is provided. The predictor may receive a search query from a user via a terminal and identify a set of candidate query rewrites associated with the search query. The predictor may then extract a set of features from advertisements associated with the query rewrites and the search query and determine a degree of relevance between the advertisements and the search query based on a prediction model. The predictor may then determine the degree of relevance between the rewrites and the search query based on the determined degree of relevance between the advertisements and the search query.

Abstract: Disclosed are apparatus and methods for ranking lines of text. In one embodiment, an intent of a query is ascertained. A relevance of each one of a plurality of lines of text of a document is determined based upon the intent of the query, content of the query, and content of each of the plurality of lines of text. The plurality of lines of text may then be ranked according to the determined relevance of each of the plurality of lines of text.

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: 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.