Search results
Results from the WOW.Com Content Network
Information retrieval is the science [1] of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds. Automated information retrieval systems are used to reduce what has been called information overload. An IR system is a ...
Indexing and classification methods to assist with information retrieval have a long history dating back to the earliest libraries and collections however systematic evaluation of their effectiveness began in earnest in the 1950s with the rapid expansion in research production across military, government and education and the introduction of computerised catalogues.
Pages in category "Information retrieval techniques" The following 53 pages are in this category, out of 53 total. This list may not reflect recent changes. A.
Human–computer information retrieval (HCIR) is the study and engineering of information retrieval techniques that bring human intelligence into the search process. It combines the fields of human-computer interaction (HCI) and information retrieval (IR) and creates systems that improve search by taking into account the human context, or through a multi-step search process that provides the ...
Query expansion (QE) is the process of reformulating a given query to improve retrieval performance in information retrieval operations, particularly in the context of query understanding. [1]
Accurate legal information retrieval is important to provide access to the law to laymen and legal professionals. Its importance has increased because of the vast and quickly increasing amount of legal documents available through electronic means. [2] Legal information retrieval is a part of the growing field of legal informatics.
Learned sparse retrieval or sparse neural search is an approach to Information Retrieval which uses a sparse vector representation of queries and documents. [1] It borrows techniques both from lexical bag-of-words and vector embedding algorithms, and is claimed to perform better than either alone.
An information retrieval technique using latent semantic structure was patented in 1988 [2] by Scott Deerwester, Susan Dumais, George Furnas, Richard Harshman, Thomas Landauer, Karen Lochbaum and Lynn Streeter. In the context of its application to information retrieval, it is sometimes called latent semantic indexing (LSI). [3]