Application of Naive Bayes Classifier for Information Extraction

Nikhil Ranjan Nayak

No z7q2e, OSF Preprints from Center for Open Science

Abstract: Information retrieval (IR) is the activity of obtaining information resources that are relevant to an information need from a collection of those resources. Searches can be based on full-text or other content-based indexing. Information retrieval is the science 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 software system that provides access to books, journals and other documents; stores and manages those documents. Web Search Engines are the most visible IR applications. It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’. Naive Bayes model is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.

Date: 2020-07-06
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:z7q2e

DOI: 10.31219/osf.io/z7q2e

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