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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.
Mooers received the American Society for Information Science's Award of Merit in 1978. The citation reads in part: He was a participant in early developmental work on digital computers, a researcher, author, and implementer of applications in information retrieval; and a prophet in the 1950s describing the future importance of what is now called computer networks and distributive processing ...
Search engine indexing is the collecting, parsing, and storing of data to facilitate fast and accurate information retrieval.Index design incorporates interdisciplinary concepts from linguistics, cognitive psychology, mathematics, informatics, and computer science.
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 ...
Cognitive models of information retrieval rest on the mix of areas such as cognitive science, human-computer interaction, information retrieval, and library science.They describe the relationship between a person's cognitive model of the information sought and the organization of this information in an information system.
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.
The information retrieval community has emphasized the use of test collections and benchmark tasks to measure topical relevance, starting with the Cranfield Experiments of the early 1960s and culminating in the TREC evaluations that continue to this day as the main evaluation framework for information retrieval research.