• Corpus ID: 14638345

Using TF-IDF to Determine Word Relevance in Document Queries

@inproceedings{Ramos2003UsingTT,
  title={Using TF-IDF to Determine Word Relevance in Document Queries},
  author={Juan Enrique Ramos},
  year={2003}
}
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 appears in. Words with high TF-IDF numbers imply a strong relationship with the document they appear in… 

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References

SHOWING 1-7 OF 7 REFERENCES
Information retrieval as statistical translation
TLDR
A simple, well motivated model of the document-to-query translation process is proposed, and an algorithm for learning the parameters of this model in an unsupervised manner from a collection of documents is described.
Term-Weighting Approaches in Automatic Text Retrieval
Bridging the lexical chasm: statistical approaches to answer-finding
TLDR
It is shown that the task of “answer-finding” differs from both document retrieval and tradition question-answering, presenting challenges different from those found in these problems.
Using Linear Algebra for Intelligent Information Retrieval
TLDR
A lexical match between words in users’ requests and those in or assigned to documents in a database helps retrieve textual materials from scientific databases.
A Statistical Approach to Machine Translation
TLDR
The application of the statistical approach to translation from French to English and preliminary results are described and the results are given.
Reexamining tf.idf based information retrieval with Genetic Programming
TLDR
This paper proposes a method to automatically perform a search for new tf.idf like schemes using genetic programming, and the results are evaluated in a simple usage scenario.
Cross-Language Text Retrieval with Three Languages
  • In CS-1997-16,
  • 1997