Information Retrieval Questions Long
The TF-IDF (Term Frequency-Inverse Document Frequency) weighting scheme is a numerical representation used in information retrieval to evaluate the importance of a term within a document or a collection of documents. It is widely used in search engines and text mining applications.
TF (Term Frequency) measures the frequency of a term within a document. It calculates the number of times a term appears in a document divided by the total number of terms in that document. The idea behind TF is that the more times a term appears in a document, the more important it is to that document.
IDF (Inverse Document Frequency) measures the rarity of a term across the entire document collection. It calculates the logarithm of the total number of documents divided by the number of documents containing the term. The IDF value decreases as the term appears in more documents, indicating that common terms are less informative than rare terms.
The TF-IDF weight of a term is obtained by multiplying its TF value with its IDF value. This weight reflects the importance of the term in a specific document relative to the entire collection. Terms with higher TF-IDF weights are considered more significant and relevant to the document.
The TF-IDF weighting scheme helps in ranking and retrieving documents based on their relevance to a given query. When a user submits a query, the search engine calculates the TF-IDF weights for the terms in the query and compares them with the TF-IDF weights of the terms in the documents. Documents with higher matching TF-IDF weights are considered more relevant and are ranked higher in the search results.
Overall, the TF-IDF weighting scheme provides a way to measure the importance of terms in documents, taking into account both their frequency within a document and their rarity across the document collection. It is a fundamental technique in information retrieval that helps improve the accuracy and relevance of search results.