Assertion: document's word | /r/AtLocation | document
Facets:
value: many times
type: TEMPORAL
Source sentences:
text: This is done by multiplying two metrics: how many times a word appears in a document, and the inverse document frequency of the word across a set of documents.
text: 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.
text: In each document, the word "this" appears once; but as the document 2 has more words, its relative frequency is smaller.
text: So, if the word is very common and appears in many documents, this number will approach 0.
text: It works by increasing proportionally to the number of times a word appears in a document, but is offset by the number of documents that contain the word.
text: However, if the word Bug appears many times in a document, while not appearing many times in others, it probably means that it’s very relevant.
text: There are several ways of calculating this frequency, with the simplest being a raw count of instances a word appears in a document.