Essays on Information Retrieval, Inverse Document Frequency Coursework

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The paper "Information Retrieval, Inverse Document Frequency" is an outstanding example of management coursework.   With the development of information technology more and more data is being stored in electronic and other forms. Finding the correct data especially from the electronically stored information is becoming more important by the day. Information research aims at developing models and algorithms for the purpose of information retrieval from document repositories. For effective Information Retrieval, it is necessary to understand how search engines work. Information Retrieval (IR) is defined as the science of searching for information in documents, searching for documents themselves, searching for metadata which describes the document or searching within databases, whether relational stand-alone databases or hypertextually-networked databases such as ‘ World Wide Web’ .(Wikipedia) Process of Retrieval Information Retrieval is the retrieval of unstructured data.

It could be the retrieval of documents or specific information in the documents. It could also be the retrieval of speech or images. When the user needs some information, he converts it into a query as a formal statement and the Information Retrieval system finds the relevant information. Most of the information retrieval is done from texts.

The query formation is based on ‘ bags of words’ , which is a phrase or group of words. Due to the constant growth of text documents, ‘ bags of words’ do not get precision in the results. Synonymous words are one type of challenge. Another challenge is that many times a group of words may have a totally different meaning to the individual words. For example, Hot Dog as a group of words has no similarity to Hot or Dog. Ranked retrieval Ranked retrieval starts with a query and calculates relevance score between the query and every document.

It sorts documents by their score and presents the top-scoring documents to the user. Score computing is done in three stages: - • Quorum scoring • Term frequency (TF) weights • Inverse Document Frequency (IDF) weights For example, if the original query is “ The Amazonian rain forests” , in Case normalization it will be like “ the amazonian rain forests” .



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