Abstract: Cross-lingual automatic query annotation technique is described, for example, to classify online search queries in Arabic as being of commercial intent, without the need to use human judged Arabic queries. In examples, a query classifier available for a source language (such as English) is used to produce a query classifier for a target language (such as Arabic, German, French). In various examples, a target language query log and target language documents may be used to enable target language and target culture dependent queries to be classified. In various examples a click graph with edges weighted by click frequency is used to infer class membership of unlabeled target language queries from target language documents. In examples the target language documents may be classified using a supervised or semi-supervised classifier. In various examples the automatically labeled target language queries are used to train a target language query classifier for information retrieval and/or advertising.

Abstract: A system for retrieving/identifying a document comprising text stored in a document repository is described. A memory stores a graphical structure comprising a first plurality of nodes each representing a person, and a second plurality of nodes each representing a document in the document repository, the nodes being connected by edges according to automatically observed interactions between the represented people and documents. At least some of the nodes have one or more annotations each denoting a topic. A node relatedness calculator computes distances between nodes of the graphical structure using the topic annotations. An input receives an identifier of a user who is represented by one of the first plurality of nodes. An identifier/retriever identifies one or more documents from the document repository by using the identifier and using the computed distances between nodes.

Abstract: A system for retrieving/identifying a document comprising text stored in a document repository is described. A memory stores a graphical structure comprising a first plurality of nodes each representing a person, and a second plurality of nodes each representing a document in the document repository, the nodes being connected by edges according to automatically observed interactions between the represented people and documents. At least some of the nodes have one or more annotations each denoting a topic. A node relatedness calculator computes distances between nodes of the graphical structure using the topic annotations. An input receives an identifier of a user who is represented by one of the first plurality of nodes. An identifier/retriever identifies one or more documents from the document repository by using the identifier and using the computed distances between nodes.

Abstract: Cross-lingual automatic query annotation technique is described, for example, to classify online search queries in Arabic as being of commercial intent, without the need to use human judged Arabic queries. In examples, a query classifier available for a source language (such as English) is used to produce a query classifier for a target language (such as Arabic, German, French). In various examples, a target language query log and target language documents may be used to enable target language and target culture dependent queries to be classified. In various examples a click graph with edges weighted by click frequency is used to infer class membership of unlabeled target language queries from target language documents. In examples the target language documents may be classified using a supervised or semi-supervised classifier. In various examples the automatically labeled target language queries are used to train a target language query classifier for information retrieval and/or advertising.