Перевод и анализ слов искусственным интеллектом ChatGPT
На этой странице Вы можете получить подробный анализ слова или словосочетания, произведенный с помощью лучшей на сегодняшний день технологии искусственного интеллекта:
- как употребляется слово
- частота употребления
- используется оно чаще в устной или письменной речи
- варианты перевода слова
- примеры употребления (несколько фраз с переводом)
- этимология
IDF (Inverse Document Frequency model) - перевод на Английский
Ex: This study compares the Two-Poisson (2P), Inverse Document Frequency (IDF) and Discrimination Value (DV) models of document representation.
Ex: Most relationships should be shown in both their direct and inverse forms.
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* inverse cube law = ley de relación exponencial inversa al cubo
* Inverse Document Frequency model (IDF) = método de la Inversión de la Frecuencia de los Documentos (IDF)
* inverse power law = ley exponencial inversa
* inverse relationship = relación inversa, relación inversamente proporcional
* inverse square law = ley de relación exponencial inversa al cuadrado
* significant inverse relationship = relación inversa significativa, relación inversamente proporcional significativa
![The inverse of this [[cubic function]] has three branches.](https://commons.wikimedia.org/wiki/Special:FilePath/Inversa d'una cúbica gràfica.png?width=200 "The inverse of this [[cubic function]] has three branches.")
Википедия
In information retrieval, tf–idf (also TF*IDF, TFIDF, TF–IDF, or Tf–idf), short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. It is often used as a weighting factor in searches of information retrieval, text mining, and user modeling. The tf–idf value increases proportionally to the number of times a word appears in the document and is offset by the number of documents in the corpus that contain the word, which helps to adjust for the fact that some words appear more frequently in general. tf–idf has been one of the most popular term-weighting schemes. A survey conducted in 2015 showed that 83% of text-based recommender systems in digital libraries use tf–idf.
Variations of the tf–idf weighting scheme are often used by search engines as a central tool in scoring and ranking a document's relevance given a user query. tf–idf can be successfully used for stop-words filtering in various subject fields, including text summarization and classification.
One of the simplest ranking functions is computed by summing the tf–idf for each query term; many more sophisticated ranking functions are variants of this simple model.
