Text similarity is to calculate how two words/phrases/documents are close to each other. That closeness may be lexical or in meaning.
Semantic similarity is about the meaning closeness, and lexical similarity is about the closeness of the word set.
Let’s check the following two phrases as an example: The dog bites the man The man bites the dog According to the lexical similarity, those two phrases are very close and almost identical because they have the same word set. For semantic similarity, they are completely different because they have different meanings despite the similarity of the word set.
Semantic similarity between two pieces of text measures how their meanings are close. This measure usually is a score between 0 and 1. 0 means not close at all, and 1 means they almost have identical meaning.
Types of Rich Text Editors In the previous post I explained what are RTEs and why are they used, in this post, I am going to throw some light on the types of RTEs, and will make you familiar with the sample content format. https://fifo.im/p/35ghmd1uxln0
Rich Text Editor In recent years, the field of Content Creation and Representation on Digital platforms has seen massive disruption. This transition has shown how companies are racing to build the best experience for content creators in the enterprise domain and trying to find innovative ways to break the traditional molds of sharing and consuming content.