Relevansi Artikel Berita Politik Berdasarkan Query Menggunakan Term Frequency Invers Document Frequency (TF-IDF)

A simple probabilistic explanation of term frequency-inverse document frequency (tf-idf) heuristic (and variations motivated by this explanation)

Inverse Document Frequency (IDF): A Measure of Deviations from Poisson

Low frequency words tend to be rich in content, and vice versa. But not all equally frequent words are equally mean!ngful. We will use inverse document frequency (IDF), a quantity borrowed from Information Retrieval, to distinguish words like somewhat and boycott. Both somewhat and boycott appeared approximately 1000 times in a corpus of 1989 Associated Press articles, but boycott is a better k...

Understanding inverse document frequency: on theoretical arguments for IDF

The term weighting function known as IDF was proposed in 1972, and has since been extremely widely used, usually as part of a TF*IDF function. It is often described as a heuristic, and many papers have been written (some based on Shannon’s Information Theory) seeking to establish some theoretical basis for it. Some of these attempts are reviewed, and it is shown that the Information Theory appr...

Using TF-IDF to Determine Word Relevance in Document Queries

In this paper, we examine the results of applying Term Frequency Inverse Document Frequency (TF-IDF) to determine what words in a corpus of documents might be more favorable to use in a query. As the term implies, TF-IDF calculates values for each word in a document through an inverse proportion of the frequency of the word in a particular document to the percentage of documents the word appear...

Using Noun Phrases and Tf-idf for Plagiarized Document Retrieval

This paper describes an approach submitted to the 2014 PAN competition for the source retrieval sub-task [7]. Both independent term and phrasal queries are generated, using either term frequency-inverse document frequency or noun phrases to select the terms.