Sentence examples for queries and documents from inspiring English sources

Google's search engines still track all the trillions of occurrences of the word "Paris" and what it is associated with in user queries and documents, but now tries to relate those words not just to each other, but to categories, like people, places, and corporations.

This work proposes the use of high-level discourse information extracted from queries and documents to improve the precision of retrieval results.

Each phase selects a set of queries and documents from the collection, called the training set and the validation set.

For the third issue, we combine the textual similarity and the temporal similarity between queries and documents in the ranking process.

To predict the relevance issue between queries and documents, Jiang et al. [24] and Yin et al. [25] propose a vector propagation algorithm on the click graph to learn vector representations for both queries and documents in the same term space.

After the last generation is finished, we also evaluate the fitness function of the best individuals in the training set using now the validation set of queries and documents.

The aim of this paper is to provide suitable graph-based semantics to this language, supporting both data structure variability and topological similarities between queries and document structures.

The keyword index search enables a legitimate queries to search the encrypted documents with an encrypted keyword over the encrypted indexes without revealing any information on the query and documents, even to the server.

In this case different representations (text and MeSH) are used, this relates to cross-lingual relevance models in which query and documents are formulated in different languages (Lavrenko et al., 2002).

After the search document space is determined relevant documents are retrieved by comparing query and document vector similarity.

Similarily, text retrieval that uses dictionary-based semantic tree to weight the words between query and document is used to find semantic similarities [15].