Termfrekvensvärde

Termfrekvensvärde, often abbreviated as TF, is a fundamental concept in information retrieval and natural language processing. It represents the frequency with which a term, or word, appears within a specific document. The basic calculation involves dividing the count of a term in a document by the total number of terms in that document. This normalization helps to account for differences in document length, preventing longer documents from having artificially high term frequencies solely due to their size.

The raw term frequency can be further refined using various weighting schemes. For instance, logarithmic term frequency, TF-log, uses the logarithm of the term count plus one. This dampens the impact of very high frequencies, acknowledging that a word appearing 100 times is not necessarily 10 times more important than a word appearing 10 times. Inverse document frequency (IDF) is another common companion to TF. While TF measures the importance of a term within a single document, IDF measures how rare or common a term is across a collection of documents. The product of TF and IDF, known as TF-IDF, is a widely used metric to assess the importance of a term to a document in a corpus, giving higher scores to terms that are frequent in a specific document but infrequent in the overall collection. TF values are crucial for tasks like document ranking, text classification, and keyword extraction.